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Facial Recognition Technology and Ethical Concerns Essay

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Face recognition refers to a method used to confirm or identify an individual’s identity using their face. The technology authenticates and identifies an individual based on sets of verifiable and recognizable data unique and specific to that individual. Facebook launched its DeepFace program in 2014, which can be used to identify two photographed faces belonging to one individual (Scherhag et al., 2019). While face recognition technology is gaining increasing application, especially by digital corporations, critics believe that storage and identity management have various ethical issues, including privacy and confidentiality. The use of face recognition technology is associated with various ethical concerns, such as lack of transparency and informed consent, racial discrimination, misinformation and bias, data breaches and mass surveillance.

Data privacy is undoubtedly the biggest ethical concern regarding the adoption and use of face recognition technology. Privacy is a key concern for people using the internet, especially social media. According to a study by Scherhag and colleagues, face recognition programs infringe on individuals’ inherent rights to remain under constant surveillance and have their images kept without their consent (Scherhag et al., 2019). For instance, in 2019, the European Commission banned the use of facial recognition technology in public spaces because of the ethical and privacy abuse associated with the technology (Scherhag et al., 2019). Privacy concerns associated with facial recognition revolve around unsafe data storage practices capable of exposing facial recognition information. Many corporations continue to host their facial recognition information on local servers with high-security vulnerabilities.

Facebook is among the digital corporations that announced to shut down its facial recognition software used to identify faces in videos and photographs. The corporation decided to delete over one billion facial recognition templates that the company has collected since its inception. There has been increasing concern about the ethics of facial recognition programs, and many questions have been raised over their accuracy, racial bias and privacy. Facebook has been facing severe criticism over the impact of this technology on users. The company was forced to bring down the program in 2019; however, users can turn the feature back on.

The decision made by Meta to shut down its facial recognition program was a right and ethical decision. Face recognition technology compromises privacy, making intrusive surveillance normal and often targeting marginalized people. The use of face recognition technology has gotten the company into various ethical issues. In 2019, Facebook was fined $6.5 billion by the US Federal Trade Commission to settle privacy complaints (Scherhag et al., 2019). The decision to shut down facial recognition software came after the corporation faced severe regulatory and legislative scrutiny over leaked user information.

The decision to bring down facial recognition technology positively impacts the company and its users. Not only will the company’s reputation grow strong, but also it will gain more users because the users will be assured of their privacy. Moreover, Facebook will not be involved in privacy complaints associated with face recognition technology. The company is now looking for a new form of identifying individuals with minimal privacy concerns—a narrower form of individual authentication.

The government and corporations should control facial recognition technology and be allowed to use it for narrower purposes. The technology is more effective and valuable when operated privately on an individual’s devices (Scherhag et al., 2019). Face recognition technology is not private, leading to severe security concerns. People have the right over their privacy, and their data can only be used with their consent. Therefore, the government must regulate the use of facial recognition technologies by organizations and businesses.

Scherhag, U., Rathgeb, C., Merkle, J., Breithaupt, R., & Busch, C. (2019). Face recognition systems under morphing attacks: A survey. IEEE Access , 7 , 23012-23026.

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Downloadable Content

Facial Emotion Recognition using Deep Learning

• Masters Thesis
• Patil, Dipesh Dilip
• Wang, Taehyung
• Wiegley, Jeffrey
• McIlhenny, Robert
• Computer Science
• California State University, Northridge
• Dissertations, Academic -- CSUN -- Computer Science.
• Facial analysis
• Feature extraction
• Convolutional neural networks
• Pattern recognition
• Deep learning algorithms
• Facial landmark detection
• Image processing
• Emotion detection
• Facial expression recognition
• Machine learning models
• http://hdl.handle.net/20.500.12680/dz010x52t
• by Dipesh Dilip Patil

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Dissertation Topics on Facial Recognition

Published by Grace Graffin at January 9th, 2023 , Revised On August 16, 2023

Introduction

The facial recognition system refers to the technology capable of identifying a person from a digital image or a video frame from a video source. There are various methods through which facial recognition systems operate; however, generally, they work by comparing facial features from the given image with the faces in a database.

Previously facial recognition technology was developed as a computer application program. However, now it is more prevalent in both IOS and Android apps and is also used in other different forms of technology such as robotics.

The facial recognition system is more commonly employed as a security mechanism than biometric systems such as fingerprint and eye recognition technology. More recent use of this technology is in the mobile world, where the latest mobile phone models have a built-in face recognition application.

Through this technology, users can easily unlock their mobile phones without having to enter their passwords. Once the systems recognise their face, their device is unlocked. Here are a few essential dissertation topics on facial recognition to explore the world of this revolutionary technology.

These topics have been developed by PhD qualified writers of our team , so you can trust to use these topics for drafting your dissertation.

You may also want to start your dissertation by requesting  a brief research proposal  from our writers on any of these topics, which includes an  introduction  to the topic,  research question , aim and objectives ,  literature review  along with the proposed  methodology  of research to be conducted.  Let us know  if you need any help in getting started.

Check our  dissertation examples to get an idea of  how to structure your dissertation .

Review the full list of dissertation topics for 2022 here.

2022 Dissertation Topics on Facial recognition

Topic 1: examining multi-dimension in facial emotion detection..

Research Aim: When it comes to communications, human expressions are extraordinary. Humans can identify it very easily and accurately. Getting the same outcome from a 3D machine is a difficult task. This is because of the present challenges in 3D face data scanning. This study will examine the facial emotion identification in humans using different multi-point for 3D face landmarks.

Topic 2: A novel face recognition system based on the fusion of LDB and HOG.

Research Aim: Face recognition has become more relevant in a variety of situations involving visual security systems. This study will provide a novel face recognition method based on the fusion of LDB and HOG. For this study, many techniques will be employed and will also improve the drawbacks of poor accuracy and eliminate the issues that the dimension of the general fusion causes.

Topic 3: Face recognition techniques- Challenges and implementation.

Research Aim: Various biometric applications are being used in our daily activities for recognising, such as eye recognition, fingerprint, and face recognition. Facial recognition is one of the essential issues in AI and modern technology. This study aims to find the challenges and implementation of facial recognition techniques. This research will review different previous studies to analyse various techniques and challenges for recognition.

Topic 4: Facial recognition while using masks in mobile Phones.

Research Aim: Due to the Covid-19 pandemic, the use of face masks was mandatory as a safety precaution. This has generated many issues for the facial recognition systems. This study will focus on the development of the facial recognition system when people are wearing a face mask through various frameworks. A face detector will be employed, and through different stages, we will analyse whether the face can be detected or not when wearing a mask.

Topic 5: Assessing Low and High-resolution images reconstruction using different methods.

Research Aim: Deep learning in computer vision and advancements in technology have made low and high-resolution image reconstruction possible. Image reconstruction is a technical procedure; it has a significant influence on image quality. This study aims to examine the low and high reconstruction of images using various methods. Furthermore, it will analyse the best method used for image reconstruction and help restore pictures successfully. It will also focus on previous studies and help understand how it has evolved in these years.

Covid-19 Facial Recognition Research Topics

Topic 1: facial recognition technology and covid-19.

Research Aim: This study will focus on the increasing market of facial recognition technology and its use to combat the Coronavirus pandemic.

Topic 2: Facial recognition and biometric contact system during COVID-19

Research Aim: This study will show the role of facial recognition and contact in the Biometric system during COVID-19.

Topic 3: An Introduction to Facial Recognition Technology

Research Aim: Facial Recognition technology is biometric software that maps the facial features of an individual mathematically and stores them as data of a face print. With the help of deep learning algorithms, the software recognizes and stores whether the image is a live capture or a digital image. Whenever an individual utilizes the software, the face is verified through the stored images in the system. If it matches the stored image, the individual is granted access. This dissertation will focus on the basics of facial recognition technology. The software will be discussed in detail. The various characteristics of the system, how it works, features it encompasses, uses, and benefits of the system, including the drawbacks, will be discussed in this research. In short, this research will be a complete guide regarding facial recognition technology.

Topic 4: How do Facial Recognition Systems Work?

Research Aim: To keep up with the latest trends and technologies, and to get the maximum benefits, we must know how a specific technology or software functions. Facial recognition systems run on an algorithm. They map a particular device, photo, ID against a person concerning its facial features.

Thus, the next time a similar image appears on the software, the face is recognised, and the user is granted access. However, the whole technology is not as easy as it sounds. This research will delve deep into how the software works through algorithms and what aspects are considered by the system.

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Topic 5: Exploring High-Performance Face Detection Methods

Research Aim: Facial recognition technology employs different methods to capture and store images. These images are stored in the system for future use, and when a user uses the feature the next time, the idea is matched with the ones stored in the system.

If and when the system recognises the image, the user is granted access. The whole process includes three different methods for storing images as data. The first method is preprocessing, the second is feature extraction, and the third is classification.

The best method is determined based on technology, the system, and its utilisation. This research will explore all these three methods in detail and understand which way is chosen under which circumstance. All three forms may also be used together by the software for storing one image. Thus, all the details will be discussed in this dissertation.

Topic 6: Facial Recognition Technology in Surveillance: How Effective is it?

Research Aim: With the increasing trend of facial recognition in our everyday lives, this technology has increased in the surveillance industry. Many companies now depend on facial recognition technology to enhance their organisation’s safety.

In addition to this, this technology has also increased in homes where people have deployed facial recognition systems to ensure the safety of their living places. This research will explore in-depth how effective facial recognition is in the surveillance industry.

The study will include specific examples of how facial recognition is used for surveillance and evaluate its effectiveness.

Topic 7: Facial Recognition Algorithms: An Overview

Research Aim: Facial recognition technology relies on the use of algorithms. Without algorithms, the system cannot function. Thus, to understand the system, the algorithms must be thoroughly understood.

This research will focus on the algorithms that the system deploys to help understand how images are captured, stored, and recognized by the system to grant access to a user. These algorithms are complex in nature. Thus the research will provide the basics of it to make sure that the readers can easily understand the system.

Topic 8: The Future of Facial Recognition Technology

Research Aim: Facial Recognition is one of the most advanced technologies of the modern sciences. Many computer devices make use of this technology to enhance security as well as the user experience.

This research will discuss how this technology has successfully helped people and companies overcome their security issues, what the future holds for this technology and the benefits of implementing facial recognition.

A comparison will also be drawn with other security measures such as fingerprint and manual password entering systems. This research will provide a detailed analysis of how facial recognition technology has performed compared to other security measures and whether or not it has been successful in terms of security.

Moreover, the research will also discuss the future of this technology, how it can be improved, how and where it can be implemented, and how this system can prove to be even more beneficial for its users.

Important Notes:

As a student of facial recognition looking to get good grades, it is essential to develop new ideas and experiment with existing facial recognition theories – i.e., to add value and interest in your research topic.

Facial recognition is vast and interrelated to so many other academic disciplines like   Facebook , Instagram , Cryptocurrency , Twitter , civil engineering , facial recognition , construction ,  project management , engineering management , healthcare , finance and accounting , artificial intelligence , tourism , physiotherapy , sociology , management , and project management , graphic design , and nursing . That is why it is imperative to create a project management dissertation topic that is articular, sound, and actually solves a practical problem that may be rampant in the field.

We can’t stress how important it is to develop a logical research topic based on your entire research. There are several significant downfalls to getting your topic wrong; your supervisor may not be interested in working on it, the topic has no academic creditability, the research may not make logical sense, and there is a possibility that the study is not viable.

This impacts your time and efforts in writing your dissertation as you may end up in the cycle of rejection at the initial stage of the dissertation. That is why we recommend reviewing existing research to develop a topic, taking advice from your supervisor, and even asking for help in this particular stage of your dissertation.

While developing a research topic, keeping our advice in mind will allow you to pick one of the best facial recognition dissertation topics that fulfil your requirement of writing a research paper and add to the body of knowledge.

Therefore, it is recommended that when finalizing your dissertation topic, you read recently published literature to identify gaps in the research that you may help fill.

Remember- dissertation topics need to be unique, solve an identified problem, be logical, and be practically implemented. Please look at some of our sample facial recognition dissertation topics to get an idea for your own dissertation.

How to Structure your Facial Recognition Dissertation

A well-structured dissertation can help students to achieve a high overall academic grade.

• A Title Page
• Acknowledgements
• Declaration
• Abstract: A summary of the research completed
• Table of Contents
• Introduction : This chapter includes the project rationale, research background, key research aims and objectives, and the research problems. An outline of the structure of a dissertation can also be added to this chapter.
• Literature Review : This chapter presents relevant theories and frameworks by analysing published and unpublished literature available on the chosen research topic to address research questions . The purpose is to highlight and discuss the selected research area’s relative weaknesses and strengths whilst identifying any research gaps. Break down the topic, and binding terms can positively impact your dissertation and your tutor.
• Methodology : The data collection and analysis methods and techniques employed by the researcher are presented in the Methodology chapter which usually includes research design , research philosophy, research limitations, code of conduct, ethical consideration, data collection methods, and data analysis strategy .
• Findings and Analysis : Findings of the research are analysed in detail under the Findings and Analysis chapter. All key findings/results are outlined in this chapter without interpreting the data or drawing any conclusions. It can be useful to include graphs, charts, and tables in this chapter to identify meaningful trends and relationships.
• Discussion and Conclusion : The researcher presents his interpretation of the results in this chapter, and states whether the research hypothesis has been verified or not. An essential aspect of this section of the paper is to draw a linkage between the results and evidence from the literature. Recommendations with regards to implications of the findings and directions for the future may also be provided. Finally, a summary of the overall research, along with final judgments, opinions, and comments, must be included in the form of suggestions for improvement.
• References : This should be completed following your University’s requirements
• Bibliography
• Appendices : Any additional information, diagrams, and graphs used to complete the dissertation but not part of the dissertation should be included in the Appendices chapter. Essentially, the purpose is to expand the information/data.

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Questions about Facial Recognition

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Table of Contents

Report by James Andrew Lewis and William Crumpler

Published February 3, 2021

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Concern over the misuse of facial recognition technology is one of the latest fears over technological change that have included Frankenfish, mass surveillance, chip implants, and artificial intelligence (AI). As with these earlier examples, there is both confusion and exaggeration over potential risks. This is exacerbated by the lack of adequate privacy protections in the United States and the rapid pace of technological change, which can create a sense of uncertainty about risk. Broader social and political concerns over race and policing also shape the debate on facial recognition.

We reviewed the most salient of these concerns for accuracy and for their implications for policymaking, and came to several conclusions. Our first conclusion is that to reduce concerns about facial recognition, Congress needs to pass effective privacy legislation to govern digital technologies. Facial recognition requires access to personally identifiable information (PII). The United States already has extensive rules governing law enforcement access to data and collection of evidence. These need to be extended and, in some instances, modified for new technologies such as facial recognition. But rules for facial recognition do not need to wait for national privacy legislation, since guidelines can be based on existing legal authorities.    

A second conclusion is that improvements in facial recognition technology, especially in how algorithms are developed and trained, will continue to reduce the risks of error and bias. Like all new technologies, continued improvement reduces risk, and concerns based on how facial recognition technology worked even a few years ago are now out of date. To help improve public understanding of facial recognition, we have reviewed the following questions to address some of the leading concerns.

Is facial recognition racially biased?

Demographic differences in facial recognition accuracy rates have been well-documented, but the evidence suggests that this problem will disappear as the technology improves.

The most thorough investigation of the demographic effects of facial recognition was conducted by the National Institute of Standards and Technology (NIST) in 2019. NIST found that a majority of algorithms exhibited significant demographic differences in accuracy rates. However, NIST also came to several encouraging conclusions. The first is that differences between demographic groups were far lower for algorithms that were more accurate overall. This means that as facial recognition systems continue to improve, the effects of bias will be reduced. Even more promising was that some algorithms demonstrated no discernible bias whatsoever, indicating that bias can be eliminated entirely with the right algorithms and development processes.

One of the most important factors in reducing bias appears to be the selection of training data used to build algorithmic models. If facial recognition algorithms are trained on datasets that contain very few examples of a particular demographic group, the resulting model will be worse at accurately recognizing members of that group. This may be why NIST found that some algorithms developed in China performed better on Asian faces. EU proposals for regulatory frameworks for facial recognition include requirements that training data reflect “all relevant dimensions of gender, ethnicity and other possible grounds of prohibited discrimination.” This is a useful precedent for the United States.

In addition to better training data, demographic differences can also be reduced by improving the quality of the images being captured. An assessment of 11 commercial facial recognition systems by the Department of Homeland Security (DHS) found that the skin reflectance was a better predictor of accuracy differences than the self-reported race of the subjects. This indicates that higher-quality cameras and better image capture could make a difference in eliminating bias. Like NIST, DHS found that the most accurate algorithms had an almost negligible demographic effect, further supporting the conclusion that improvements in algorithm and hardware quality will reduce bias in these systems.

Does the use of facial recognition increase the risk of false arrest?

To date, there have been three reported instances of false arrests that were based in part on facial recognition out of roughly 10 million arrests annually. In these cases, facial recognition was used to analyze footage of a crime and generate a suspect or list of suspects based on a comparison with criminal databases or ID registries. The results of this analysis were then turned over to investigators, who asked witnesses to corroborate the matches. In the cases of Robert Williams , Michael Oliver , and Nijeer Parks , both the facial recognition analysis and subsequent witness corroborations were incorrect and led to their arrests.

These instances are obviously regrettable, and more should be done to prevent similar errors from occurring in the future. But critics citing these cases often gloss over the role that human operators and witnesses had in confirming the findings of the facial recognition systems and seem to imply that the alternative—identification by humans alone—is a superior and less biased way to achieve the same results. This does not seem to be the case, because there are thousands of false arrests not based on facial recognition.

U.S. jurisdictions using the technology do not consider a positive match in a facial recognition system as sufficient to justify an arrest. Police only use facial recognition to generate leads on potential suspects. These leads have to be followed up on with additional evidence gathering and corroboration with witnesses before they can be used to justify taking someone into custody. The New York Police Department, for example, stated that in its investigations, “No one has ever been arrested based solely on a positive facial recognition—it is a lead, not probable cause.” Similarly, the Department of Justice declared that “the FBI uses the technology to produce investigative leads, but nothing more.”

The most valuable lesson to be learned from the three known instances of false arrests is how important better training and procedures are for human investigators to reduce the risk of misidentification from low-quality searches or “ automation bias ,” which is the propensity for humans to prefer information from automated systems and ignore contradictory information. In the case of Robert Williams, for example, police used an extremely low-quality image to identify potential suspects, and the arrest was made after Mr. Williams was identified in a line-up by a contractor who had only seen the grainy security camera footage of the crime.

Properly trained analysts following clear guidelines would not use images of such low quality, and properly trained investigators would have known that more corroborating evidence should have been gathered before making an arrest. The Detroit Police Department has since stated after Mr. Williams’ arrest that they have updated their policies for facial recognition use to prevent such mistakes in the future. If police departments institute higher standards around how facial recognition is used, much of the risk associated with misidentification and false arrest can be mitigated. Better policies can allow us to take advantage of the technology’s benefits while reducing errors.

Is facial recognition accurate enough for law enforcement use?

The answer to this question depends on what kind of use is envisioned and whether there are clear rules governing that particular use of facial recognition. 

Facial recognition technology has improved rapidly over the past several years. In ideal conditions, facial recognition systems have extremely high accuracy. As of December 2020, the best face identification algorithm has an error rate of just 0.1 percent . This degree of accuracy requires consistency in the images’ lighting and positioning and ensuring that the facial features of the subjects are clearly visible and not obscured.

In real-world deployments, accuracy rates can be much lower. NIST’s 2017 Face in Video Evaluation (FIVE) tested algorithms’ performance when applied to video captured in settings like airport boarding gates and sports venues. The test found that when using footage of individuals walking through a sporting venue—a challenging environment where it is difficult to capture clear images of the subjects—the algorithms being tested had accuracies ranging between 36 percent and 87 percent, depending on camera placement.

The NIST results demonstrate a major issue with facial recognition accuracy—the wide variation between vendors. The top algorithm achieved 87 percent accuracy at the sporting venue, but the median algorithm achieved just 40 percent accuracy, with both algorithms using imagery from the same camera. NIST’s more recent tests have found that some facial recognition providers on the market may have error rates orders of magnitude higher than the leaders.

While a few leading vendors have developed powerful, highly accurate facial recognition algorithms, the average provider still struggles to achieve similar reliability. This makes it difficult to come to general conclusions—either positive or negative—about the accuracy of facial recognition. Given the differences among systems, policymakers need to consider the circumstances of deployment and the algorithm being used to fully understand risk. 

Stronger safeguards on facial recognition use are necessary. Legislation can clarify the standards required for different law enforcement use cases, including real-time monitoring, retroactive identification, and recognition based on body cams or images taken using mobile devices. Guidelines on when different sources of images, including arrest photo databases or state or federal identification databases, can be used for criminal investigations are needed. Requirements for human review are also needed to ensure that police do not act based on an apparent facial recognition match without substantial corroboration. Finally, transparency requirements would ensure that defendants are told when facial recognition was used as part of the investigative process and allow them to challenge these techniques in court. These safeguards can ensure that the use of facial recognition does not violate citizens’ rights.

Are there risks in using facial recognition technology for travel?

Many countries already deploy facial recognition technologies in airports, train stations, and border crossings. Its use has become the global norm independent of U.S. decisions. Facial recognition systems (both government and private) are increasingly common ways of checking passengers in before flights. These systems streamline the ticketing and boarding process and make it more secure. Facial recognition is also becoming common at land borders and other entry points in many nations, where immigration officials are using it to keep records of people who enter the country. These deployments provide convenience and security, as facial recognition processing allows travelers to make their way through checkpoints much more quickly and allows officials to more effectively monitor for known threats.

A study by Delta Air Lines at Atlanta Hartsfield Airport found that the majority of its customers would rather use facial recognition technology instead of manual processes at boarding. Seventy-two percent preferred the curb-to-gate facial recognition experience (which significantly cut the time it takes to get from curb to gate) and 93 percent had no issue using facial recognition technology for boarding. Passengers had the option to opt-out and go through the normal screening process, but less than 2 percent of customers chose to opt-out. This system replaced the existing manual inspection of photo IDs, and passenger data is not stored.  

The risk to privacy in these deployments comes from how data collected in the travel process is stored and used for other purposes. Most people would not support the use of facial recognition technology to prevent those with unpaid traffic tickets or other minor infractions from boarding a plane, but they would support its use to identify known terrorists. And while most support terrorist screenings, they would not support the data collected by these systems being sold to private companies for marketing. As with other facial recognition uses, there is a need for clear and transparent rules on the collection and use of data to ensure that the risks of abuse and misuse are minimized.

Is facial recognition technology used to surveil protestors?

Hearing that facial recognition is being used on protesters may conjure images of CCTV scanning marches and creating logs of each person in the crowd for the police to follow up on later. However, this is not what is actually happening in the United States. It is true that facial recognition has already been used by police to run searches on individuals involved in protests. This raises obvious and legitimate concerns about the potential risks to peaceful protesters, but going past the headlines shows that examples of this happening in the United States have actually been more limited in scope than is usually portrayed. Facial recognition has only ever been used by U.S. police to identify individuals suspected of criminal activity, never to passively monitor demonstrators.

In Baltimore , when facial recognition was used during the 2015 protests following the death of Freddie Gray, police used it to compare some protesters’ social media profile pictures against a list of individuals with outstanding arrest warrants. When it was used by D.C. police in 2020, it was only after an officer had identified an image on Twitter of the man who had pulled him to the ground and punched him during a protest. In 2020 in Miami , facial recognition was also used to identify a protester, but only as part of an investigation into an individual who threw rocks at police officers. In 2021, it was used to identify rioters at the U.S. Capitol who had violently sought to prevent the certification of the 2020 election results. These cases show that the usage of the technology has been limited to investigating criminal activity rather than targeting protesters indiscriminately. There are legitimate concerns about chilling effects rising from these uses, so policymakers should ensure that rules for using facial recognition are carefully defined and consistent with civil rights. 

There has been some progress in establishing such rules and guidelines to prevent abuses against protesters. The recent Washington State law regulating facial recognition, for example, bans law enforcement from using it to “create a record describing any individual's exercise of rights guaranteed by the First Amendment of the United States Constitution.” The Department of Justice has similarly announced that “federal law enforcement will not use Facial Recognition Technology to unlawfully monitor people for their political views or based solely on a person’s exercise of First Amendment rights.” This is a constraint that could easily be put into place at a national level to prevent any risks of unconstrained surveillance against protesters.

If the U.S. government uses facial recognition technology, will that place us on a slippery slope to becoming a surveillance state like China?

The emergence of facial recognition has led to concerns by some that the technology could expand invasive government surveillance. These concerns have been heightened by the growing popularity of conspiracy theories and fears of new technology.  These concerns are unsupported by evidence and ignore existing safeguards on government surveillance, including the extensive legal framework that applies to government action, the United States’ democratic culture, the strength of its institutions and federal structure, and its observance of the rule of law. These remain strong and limit risk.

Fear of facial recognition is part of the mounting anxiety over technological change, such as the use of AI, and reflects larger societal concerns about policing, race, and democracy. These are major challenges for U.S. society, but they are created by human action, not technology. The discussion of other AI technologies lies outside the scope of this paper, but in previous instances, progress in the development of automation and autonomous technologies (and facial recognition is a form of AI) has led to social and economic improvement as the right rules and “guardrails” have been put in place.

The determining factor is not whether a country uses facial recognition but whether it has strong institutions and a culture that protect the rule of law and individual rights. For facial recognition, what is needed is better rules governing the use of technology and rules governing law enforcement’s use of digital data now available from new technologies. 

How will the use of facial recognition by private companies affect privacy?

The United States has a patchwork of privacy rules with many gaps and few limitations on how companies use the data they collect or buy from others. In this, the treatment of facial recognition data is little different than the data created by other digital technologies. Since location data, search data, purchase history data, social media use, contact data, and credit data are already collected in a largely unregulated fashion and can be correlated with other data or sold to other companies, limiting only facial recognition data does little to improve privacy. Commercial scenarios for facial recognition technology suggest that its value often comes from being linked to data collected by other means.  

Businesses can take advantage of facial recognition’s capability for remote monitoring to collect highly detailed information about people’s movements and behaviors without individuals’ knowledge and share or sell that data. For example, in-store cameras could track what goods a customer looks at and what is purchased and send targeted ads to the indecisive. Casinos already make extensive use of facial recognition technology to identify and classify customers by risk and preferences, allowing them to prevent the entry of known gambling addicts or troublesome customers while offering preferred customers special perks. 

A new issue, created in part by the response to Covid and the greater use of computer networking technologies to characterize a subject’s behavior, is the use of software and cameras to monitor employees and students as they work remotely. Commercially available software allows employers and teachers to tell when someone is not paying attention or cheating on an exam and even track web-browsing and monitor keystrokes. This exceptionally intrusive use of technology (which can include facial recognition technology) is not governed by any rules in the United States. Risks to digital privacy in the United States, including facial recognition, can be reduced by passing comprehensive privacy legislation that provides transparency and creating rules on what is being collected and how it is being used.

How is facial recognition different from facial characterization?

Facial recognition is a subfield of AI that creates software systems to identify and compare faces in images and video. In practice, facial recognition tools can be thought of as a way to evaluate a claim. Those claims can be anything from “is this person who they say they are?” to “is this person contained within this database?” 

Facial recognition can be distinguished from face characterization (or analysis), where the purpose is not to compare two images, but to classify a single face according to gender, age, emotion, or some other category. Though facial analysis can sometimes be packaged together with facial recognition tools, it is a distinct technology with its own separate development process, uses, and risks. Face characterization is often conflated with facial recognition in popular reporting, leading to substantial confusion.

The risks posed by each technology are different and depend heavily on the context of use. Facial recognition systems can range from innocuous software that lets you sign in to your phone using your face to real-time surveillance systems. Similarly, face characterization can be used to anonymously count the number of men versus women who enter a store or to allow cameras in China to send alerts to the police when it identifies someone as a Uighur.

This shows that risk is actually not created by the technology but by the purpose for which it is used, what data is collected and retained, and whether that data is used for other purposes. This points to the need for context-specific safeguards. Policymakers would be better served by addressing broader issues relating to data collection, retention, and use through general privacy regulation. 

Moving Ahead

Facial recognition is the latest technology to become a lightning rod for larger social concerns. It lies at the intersection of powerful and legitimate political concerns over privacy, policing, and AI. The source of concern is not the technology per se but larger trends in society and a technology-driven environment that can seem impervious to control.  

This kind of alarm is not new. Society’s views of technology have often veered from fearful to optimistic. Steam trains were greeted in the 1830s with reports that the human frame could not withstand speeds over 45 miles an hour. The first cars in the United Kingdom were required to have a person with a red flag walk before them to warn pedestrians and horses. The experiences of the twentieth century raise understandable concerns about the dark side of technology, and these have only been reinforced by the pervasive erosion of privacy online resulting from digital technologies.    

We continue to emphasize the importance of national privacy legislation as the foundation for protections in the use of facial recognition. Our research has made clear that the use of facial recognition technologies requires clear regulations and laws that appropriately control use and provide for accountability and transparency. This is best done at the federal level, to ensure standard practices and protection in all jurisdictions.   

Some of these new rules and required best practices are specific to facial recognition, such as defining limitations in how it is used for criminal investigation and arrests. Others should be part of a larger national approach to privacy protection, including rules on the storage, use and transfer of data and measures to provide transparency in what data is being collected, how it is used, and if it is stored or shared. Some facial recognition uses (both private and governmental) could allow for citizens to opt out, as in the use for airport security screening. Other uses related to public safety should not allow for opting out, but this means those uses must be guided by a higher degree of regulation and transparency. These rules should depart from past practice, provide a degree of parity between private sector and governmental use, and apply to both government and private sector facial recognition deployments.   

Our conclusion is that risk from the use of facial recognition technology is best managed by implementing rules and safeguards appropriate for each case. We must be careful to ensure that any new rules are not based on information that is incorrect or outdated.  Technological change is not going to stop and the use of artificial intelligence in applications like facial recognition will continue to grow. We do not want to continue the precedent of allowing unregulated use of technology—the internet’s effects on privacy and security show the risk of a laissez faire approach—but we also want to avoid overregulation, since this is a proven way to stop innovation and give technological advantage to other countries. Our next reports will look at how facial recognition technologies work, the current policy and regulatory environment for facial recognition in the United States, and how policymakers should approach regulation.  

James Andrew Lewis is a senior vice president and director of the Strategic Technologies Program at the Center for Strategic and International Studies (CSIS) in Washington, D.C. William Crumpler is a research associate with the CSIS Strategic Technologies Program.

This report is made possible with support from the U.S. Department of Homeland Security.

This report is produced by the Center for Strategic and International Studies (CSIS), a private, tax-exempt institution focusing on international public policy issues. Its research is nonpartisan and nonproprietary. CSIS does not take specific policy positions. Accordingly, all views, positions, and conclusions expressed in this publication should be understood to be solely those of the author(s).

© 2021 by the Center for Strategic and International Studies. All rights reserved.

James Andrew Lewis

William crumpler, programs & projects.

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Home > Dissertations and Theses > Theses and Dissertations > 3076

University of New Orleans Theses and Dissertations

Comparison of facial emotion recognition models using deep learning.

Arsany Hanin , University of New Orleans Follow

Date of Award

Degree type, degree name, degree program.

Electrical Engineering

Major Professor

Dr. Dimitrios Charalampidis

Second Advisor

Dr. Abdul Rahman Alsamman

Third Advisor

Dr. Kim Jovanovich

Facial emotion recognition is a widely studied area with applications in diverse domains such as human-computer interaction, affective computing, and social robotics. This thesis aims to improve the accuracy of facial emotion recognition models by incorporating a second neural network trained on original probabilities and probability transformation, while also comparing the performance of different techniques. The thesis begins with a thorough review of available datasets and technologies used for data collection, highlighting the challenges associated with these datasets. A detailed analysis of various facial emotion detection models, including the baseline model and its different architectures, is presented. The thesis also explores the pre-processing of datasets for binary classifiers and investigates the effects of developing an ensemble of binary classifiers.The main contribution of the thesis is the incorporation of a second neural network trained on the probabilities of binary models, along with probability transformation, to enhance the accuracy of facial emotion recognition models. Experimental results on the FER2013 dataset are presented, demonstrating the effectiveness of this approach, achieving a best accuracy of 69.4%. Additionally, the thesis compares the performance of different techniques to provide insights into their relative effectiveness in improving facial emotion recognition accuracy.The thesis concludes with a summary of the results, drawing conclusions from the analysis, and discussing future directions for further research in facial emotion recognition. The findings of this research contribute to the advancement of facial emotion recognition techniques and provide valuable insights for researchers and practitioners in the field.

The University of New Orleans and its agents retain the non-exclusive license to archive and make accessible this dissertation or thesis in whole or in part in all forms of media, now or hereafter known. The author retains all other ownership rights to the copyright of the thesis or dissertation.

Recommended Citation

Hanin, Arsany, "Comparison of Facial Emotion Recognition Models Using Deep Learning" (2023). University of New Orleans Theses and Dissertations . 3076. https://scholarworks.uno.edu/td/3076

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Facial Emotion Recognition Thesis

Identification of facial emotions for the given human face is called facial emotion recognition . Generally, face emotion is helping people to effectively communicate with other people. The prosperity of every communication basically depends upon the accuracy of facial emotion recognition. Uniformity of universal emotions is exposed by many humans to recognize each and every individual’s different expressions. Are you really confused about framing the facial emotion recognition thesis ? Then this article is exclusively meant for you!!! Let’s make it worth it!!!

In short, emotions are often called intermediate among people by means of supporting interactions. At the end of this article, you would definitely get all the relevant facts. The article is about to begin hence advising you to pay your kind attention to get the interesting facts and ideas of facial emotion recognition thesis. Come! Let us get into the handout.

Outline of Facial Emotion Recognition

Humans are capable of expressing their feelings in the form of emotions. Faces are the major feature in which emotions are widely expressed. The main usage of emotional expression helps us to recognize the intention of opponent persons.      

The emotions are different in classes such as joy, guilt, contempt, jealousy and this may vary according to the leverage of emotional conditions. In fact, people are supposed to raise or minimize their vocal tones according to the mood they trespass. There are seven emotions that are called basic emotions such as, 

Actually, it is very difficult to examine the emotional conditions expressed in digital platforms like Twitter, Facebook, and so on. 

Apart from facial expression, emotion recognition is also possible by observing the body gestures of an individual, and the modulation of voice tones would also help us. 

When hard times hits an individual, people tend to express weird and unconventional expressions through the face or body language . Thus it is, very important to recognize the emotion exactly. For this, many of the researchers of our institute and other engineers from all over the world are engaged with researches habitually. 

Here you may get questions like, where we are exactly required facial emotion recognition actually. Is this right? We think we guessed right!!! In fact, we have wrapped the answer in the following passage to make your understanding clear.

Where do we require Facial Emotion Recognition?

Generally, facial emotion recognition thesis ideas is based on widely used to observe individual personalities under several circumstances and it is widely used in the fields of,

• Secured Areas
• Industries & Retail Shops

In fact, images of human beings are being captured by surveillance cameras and other high-resolution cameras. It simply requires input or even video frames to accurately recognize the users’ emotions. HCI (Human-Computer Interface) is one of the major areas in which we need more assistance with face recognition technology .

The idea behind using face emotion recognition technology in the above-listed areas is to ensure the user’s well-being states. In driving, it exactly recognizes the drivers’ state of mind and helps to avoid accidents whereas in hospitals it is individualizing each and every patient and helps them to survive.  

In fact, the benefits of having face emotion recognition are countless. Better, you can have further explanations in these areas from our academics, if you don’t mind. Here, we would like to illustrate how to recognize facial emotions with the simplest procedures. Come on let us make the session interesting.

How Do We Recognize Facial Emotions?

• Image Preprocessing
• Feature Extraction
• Emotion Classification

These are major steps involved in recognizing human emotions in general . In fact, this can be possible by applying several techniques in each and every process. In short, image or video inputs are preprocessed to extract the features that are helping us to recognize the basic emotions as said earlier.  

Actually, we can exactly recognize the emotions by learning the message’s intensity by observing signals influenced. On the other hand, image preprocessing is one of the major techniques widely used for facial emotion recognition. 

Thus, it improves the quality of the given input (image/video) by pinpointing the area of interest & removing the artifacts in every input. So that image preprocessing is categorized under 3 main techniques. Don’t squeeze your head!!! We are actually going to tell you the same.

Image Preprocessing Techniques for Facial Emotion Recognition

• ‘Image Filtering’ Techniques
• ‘Face Detection Techniques
• ‘Normalization’ Techniques

These are the 3 key techniques being interconnected to the image preprocessing step. As it is the primary stage, it is focusing on enhancing the quality of inputs given. Here, you may shoot questions to us!!! As we are skilled mentors in the industry, we know the student’s mentality. To make your understanding better, we are also going to chit-chat the section with the feature extraction techniques discussions.

Generally, the term feature extraction in emotion recognition refers to the process of converting inputs into other appropriate forms of features. Usually, applications used for facial emotion recognition is using facial features such as mouth, eyes, eyebrows, nose as their sources to proceed further processes.

Feature Extraction Techniques for Facial Emotion Recognition

• GF- Gabor Filters
• HWT – Haar Wavelet transforms
• SVD – Singular Value Decomposition 
• FFT – Fast Fourier Transform
• DCT – Discrete Cosine Transform 
• ICA – Independent Component Analysis
• PCA – Principal Component Analysis

Itemized above are the various methods used for the feature extraction methods. The main usage of this technique is to make the system computationally very fast by means of minimizing a large amount of data into a lesser amount of feature sets. It is manipulating various technical hitches like, 

• Pose variations like angles
• Dissimilarities in facial expressions 
• Poor illumination conditions

The key idea behind feature extraction is to extract the presented exceptional (unique) features. Face emotion recognition rate is determined by the extracted unique features. Our technical crew is concurrently putting their effort again to make the face extraction techniques in a wow manner. 

Usually, developers are using both filtering and geometric features (2D & 3D) by making use of static image info. For improving system performance, we prefer you, people, to combine the several techniques above listed feature extraction techniques.

Actually, classification is the process of classifying human facial emotions through classifiers. Classifiers in the classification process are concreted with policies & diverse patterns. Non-parametric & parametric classifiers are being suggested by various engineers to plot the issues that arise in facial recognition thesis. In this regard, let us have further discussions on classification techniques in the following passage.

Classification Techniques for Facial Emotion Recognition

• Decoders & Encoders
• Convolutional Neural Networks (CNN)
• Ensemble CNN 
• Hybrid Algorithms 
• Linear Discrimination 
• Radial Basis Function
• Multilayer Perceptron
• Hidden Markov Model
• Feed Forward Neural Network

Above itemized are the various classification techniques being connected with the facial emotion recognition as well as they are irreplaceable techniques as this is fulfilling the recognition process by means of consistency . Therefore, they are compressing the false-positive rates arouse by errors.

Optimization techniques are being applied in the areas of features that are extracted. To the end, the classification process is implemented to exactly recognize the particular emotion expressed by an individual.Classification processes can be done effectively by accommodating supervised training which has the capacity to label the data.

An accurate class label is assigned to each and every input by means of classifier training accomplishments. AUs (Action Units) is one of the performances being considered in face emotion recognition like the 7 basic emotions. As of now, we have discussed the foremost fields that are needed before writing a facial emotion recognition thesis.

Now, the academics of our institutes would like to share the famous datasets widely used in the areas of facial emotion recognition for ease of your understanding. In fact, our researchers are well versed in the edges of technology and they are nailing their performance whatever comes into their hands technology-wise. Come, let’s have the next section.

Famous Datasets for Facial Emotion Recognition

• This dataset has consisted of high quality image inputs in order to provide legitimate foreground user geo-location
• It is aiding to improve the performance of emotion recognition by mapping the facial features such as mouth, nose & eyes
• It has nearly 5760 image inputs from 10 subjects/persons under 576 lighting variations for each & every resource
• SCface offers the motionless images of the human being under an uncontrolled or wild environment by five (5) surveillance cameras
• These 5 cameras are differing in their quality & it is the open-source dataset with 4160 motionless (static) images
• It is replicating the real-time activities of the world and is mainly used for experimentations by many developers
• FERET is the huge database of human face images that is acquired from the various application or system developers
• The key objective behind these datasets is to help the enforcement of law & intellectual systems with high security
• It has 1564 labeled sets of images among 14126 images which is a combination of 365 replicas & 1199 real images
• JAFFE is contained with 219 images which are based on Grayscale and pose different varieties of human face expressions (7 basics)
• It is particularly acquired from 10 Japanese females and expressed nearly 3-4 poses per single expression as well as they are rated and labeled according to the emotion projected

The above listed are the various datasets being used in the facial emotion recognition processes in general . However, there are so many datasets being integrated with the data servers over time. The spectating database is much complex; in fact, that is having the major source (images) for emotion recognition which is an internal part of the server. 

Actually, our data scientists in the concern are knowledge hulks who are dynamically performing in the digital platforms for assisting students. In fact, we are not only offering project and research guidance but also providing significant interpretations in thesis writing also. As this article is concentrating on giving content about the facial emotion recognition thesis, we are here going to let you know some essential kinds of stuff on the same to make you understand ease.

Facial Emotion Recognition Thesis Writing Guidelines for Research Career

• Indicates crisp phrase of a research idea 
• Consists of a specific research topic
• Specifies the core idea of research
• States the exact details about the topic
• Gears up the direction into problems 
• Mention the opinions about issues
• Gather prevailing issues & compare them with previous papers’ issues
• Give weightage to the current state of issues & find solutions 
• Frame rough drafts with the collections
• Make chapters from 1 to 5 or 7
• Check with grammatical & technical errors
• Refine the thesis as much as possible
• Finalize thesis with novelty & readability

The aforementioned are basic guidelines to be considered before Thesis Writing . Try to avoid imitating other formats and ideas in this area. In fact, it is not suggestible. Thus, make your simplest and unique efforts in order to light up your Facial Emotion Recognition Thesis in an incredible manner. If you still need any assistance in framing an effective thesis then you could join us at any time. 

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Congratulations to geography seniors and award winners.

Geography senior award winners.

Congratulations to all Geography graduating seniors, and a special congratulations to the following award winners:

GEORGE PERKINS MARSH AWARD

George Perkins Marsh was a member of the U.S. Congress, an author and farmer, and was the U.S. Minister to Turkey and Italy.  He graduated from Dartmouth in 1820 and is the author of the book  The Earth as Modified by Human Activity , possibly the earliest Anglophone treatise on human-environment geography.  This work eventually helped found the environmental movement in the United States.

This year's George Perkins Marsh award goes to Lily Gray  for her senior honors thesis on the use of thermal remote sensing to study Boston's urban heat island. Her work on that thesis nicely responds to Marsh's 19th-century vision of the interaction between humans, environment, and climate.

DOREEN MASSEY AWARD

Doreen Massey was a geographer of incredible breadth, with pioneering works in economic, feminist, Marxist and cultural geography. And she was centrally concerned with power relationships in all their complexity and how to challenge them when necessary. She was also fiercely committed to creating societies where there is democracy, equality and freedom, and to the creative and radical movements that might bring about such change. Accordingly, this Award goes to the student who exemplifies Massey's brilliant interdisciplinarity and socially relevant scholarship.

This year's Doreen Massey award goes to Avery Borgmann and Colin Donnelly.

One of two Doreen Massey awards goes to Avery Borgmann. Avery is passionate about things she decides to do. One of those things happens to be applying the most advanced geographic methodologies to addressing social justice issues. A prominent outcome of this passion at Dartmouth is her outstanding high honor thesis, which examines the inequality in the maternity care services in DC.

The geography department is pleased to award Colin Donnelly one of the two Doreen Massey Prizes in recognition of his excellent interdisciplinary scholarship that creatively bridges critical geography and art practice. Colin brings enthusiasm, commitment, and care to his research and scholarship. We in geography are particularly proud that we were able to draw him into the major at the last minute and we congratulate him on his excellent thesis on Queer Placemaking through art in NYC.

THELMA GLASS AWARD

Thelma Glass was a professor of geography at Alabama State University where she taught for over 40 years. She was also deeply committed to social change, having been a member of the Women's Political Council, which helped organize the Montgomery Bus Boycott in 1955-56. In addition to her academic interests in economic, cultural and physical geography, she was known around the Alabama State campus as a teacher-activist willing to put her ideals into concrete actions. Accordingly, we grant this prize to a graduating senior who best reflects Professor Glass' spirit as a scholar-activist.

This year's Thelma Glass award goes to Janine D'Souza . Since arriving at Dartmouth, Janine immediately engaged with Center for Social Impact, first as a Foundations in Social Impact Fellow, then through Strengthening Educational Access with Dartmouth (SEAD) as an educational access advisor. On her off terms, she interned at Save the Children, South Carolinians for Alternatives to the Death Penalty, and the International Rescue Committee; for her commitment to making change in the world, she has received the LGBTQIA+ Community Advocate Award and the Jan-Roberta Tarjan Award for Local Community Service.

Janine's thesis, Getting Here and Being Here: The Failed Dream of Refugee Resettlement in the U.S., was a true culminating experience, drawing together her course work, internships and extracurricular activities, and research while also conveying her immense commitment to understanding and improving the lives of some of the most marginalized people.

After a few weeks travel with friends and family, Janine will begin her new job at Perry Law Firm in New York City as a legal analyst.

RACHEL CARSON AWARD

Rachel Carson's book  Silent Spring , published in 1962, ushered in the modern environmental movement and influenced a generation of conservationists, scientists, and grassroots activists. She was a vocal and articulate advocate for the environment who argued that unchecked industrial activities were engendering catastrophic change to the natural world.  This Award is given to the student who best represents Rachel Carson's passion for the environment, intelligence, and her commitment to political change.

This year's Rachel Carson award goes to Zanna Gulick-Stutz . Zanna's academic work, as well as her professional and personal experiences on and around rivers, make her an ideal recipient for the Rachel Carson award.

Her thesis, which focuses on how climate change intersects with the environmental politics and knowledge controversies surrounding the possible breaching of the Lower Snake River dams, raises questions about society's relationship with, and obligation to, the natural environment – questions that were central to the work of Rachel Carson. Zanna's research builds on that work and advances our understanding of the environmental challenges confronting our world in the 21st century

ALEXANDER VON HUMBOLDT AWARD

Humboldt was a broad-ranging thinker of the late 18th and early 19th century who sought to understand nature and society as a complex, holistic entity.  His landmark work  Kosmos  established him as a pioneer of biogeography and landscape analysis, and was an early statement on carving out geography as a distinct discipline that combined the human and physical sciences.

This year's Alexander von Humboldt award goes to Richard Lytle . Richard, following in that tradition but perhaps slightly less prolific than Humboldt, travelled to one distant continent and wrote one senior honor's thesis but it was an outstanding piece of scholarship that has earned him the Alexander von Humboldt Prize.

richard.jpg

LEAH HOROWITZ AWARD FOR SOCIAL JUSTICE

This award goes to the student whose time at Dartmouth epitomizes President Dickey's charge that "the world's problems are your problems.'"  It honors Leah Horowitz, a Geography major who in 2009 died tragically in Ghana, where she worked for a development agency. As a student Leah taught and humbled us; she combined great intellect with wanting to know how what she learned  here  was going make a difference in the larger world.  She lost no time in doing that, first as an AmeriCorps volunteer and then working for the International Food Policy Research Institute where, notably, she asked to be transferred from DC to the field office in Ghana. It is to honor Leah's memory—and her deep commitment to social justice—that we give this award. 

This year's Leah Horowitz Award for Social Justice goes to Solange Acosta Rodriguez . Solange is a tireless activist working across a wide range of issues and places, including Indigenous rights and energy justice in both Chile and Norway. As everyone is probably aware, she is also a prominent campus voice for the rights of peoples around the world experiencing oppression and injustice. We are extremely proud to have her as a Major, and believe she embodies Leah's commitments to emancipation and social justice.

solange.jpg

BOB HUKE AWARD

Bob Huke graduated from Dartmouth in 1948, went on to earn his PhD at Syracuse, and returned to Dartmouth to teach and research in 1953. He was a fabulous teacher and mentor to many students. His research focused on food and population in his teaching and research and spent many winter terms at IRRI—the International Rice Research Institute in Los Banos, Philippines—mapping and deepening his understanding of the cultures of rice farming. Above all else, Bob is remembered in our department for his enthusiasm for Geography. Accordingly, this award recognizes the student best embodying Bob Huke's spirit and passion for Geography.

This year's Bob Huke award goes to Avery Fogg, Wenhan Sun, and Lily Ding .

CLASS OF '76 AWARD

The Geography Stretch '76 Fund is an endowment established by alumni who attended the 1976 "geography stretch" (a field course that taught geographic skills while traveling in the US) to help support the study of Geography at Dartmouth College. This award is given to the student who best exemplifies the spirit of the stretch by never being afraid to face important challenges and adventures.

This year's Stretch award goes to Keelia Stevens  and  Joseph Earles.

THE GUIDO R. RAHR '51 AWARD FOR EXCELLENCE IN GEOGRAPHY

Guido R. Rahr '51 came from a family of conservationist-philanthropists. In the 1980's, the Rahr Foundation—in honor of Guido's love of geography—donated a tract of land in Oregon to the College with the intent of supporting the Department. The land was eventually sold and the endowment has grown to the point where it now supports a range of activities in the department, including most obviously the Rahr GIS lab. This Award goes to the student who best represents Guido's passion for cartography and spatial science.

This year's Rahr award goes to Ishika Jha.

GEOGRAPHY AWARD OF EXCELLENCE

Geography Award of Excellence is an Excellence in Scholarship Award to an outstanding senior geography major with the highest departmental GPA.

This year's award goes to Avery Borgmann for her outstanding 4.0 GPA standing.