skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: Evaluation of Gender Bias in Facial Recognition with Traditional Machine Learning Algorithms
The prevalent commercial deployment of automated facial analysis systems such as face recognition as a robust authentication method has increasingly fueled scientific attention. Current machine learning algorithms allow for a relatively reliable detection, recognition, and categorization of face images comprised of age, race, and gender. Algorithms with such biased data are bound to produce skewed results. It leads to a significant decrease in the performance of state-of-the-art models when applied to images of gender or ethnicity groups. In this paper, we study the gender bias in facial recognition with gender balanced and imbalanced training sets using five traditional machine learning algorithms. We aim to report the machine learning classifiers which are inclined towards gender bias and the ones which mitigate it. Miss rates metric is effective in finding out potential bias in predictions. Our study utilizes miss rates metric along with a standard metric such as accuracy, precision or recall to evaluate possible gender bias effectively.  more » « less
Award ID(s):
1900087
PAR ID:
10358566
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
IEEE Symposium Series on Computational Intelligence (SSCI)
Page Range / eLocation ID:
1 to 7
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; (, Computers)
    null (Ed.)
    The research aims to evaluate the impact of race in facial recognition across two types of algorithms. We give a general insight into facial recognition and discuss four problems related to facial recognition. We review our system design, development, and architectures and give an in-depth evaluation plan for each type of algorithm, dataset, and a look into the software and its architecture. We thoroughly explain the results and findings of our experimentation and provide analysis for the machine learning algorithms and deep learning algorithms. Concluding the investigation, we compare the results of two kinds of algorithms and compare their accuracy, metrics, miss rates, and performances to observe which algorithms mitigate racial bias the most. We evaluate racial bias across five machine learning algorithms and three deep learning algorithms using racially imbalanced and balanced datasets. We evaluate and compare the accuracy and miss rates between all tested algorithms and report that SVC is the superior machine learning algorithm and VGG16 is the best deep learning algorithm based on our experimental study. Our findings conclude the algorithm that mitigates the bias the most is VGG16, and all our deep learning algorithms outperformed their machine learning counterparts. 
    more » « less
  2. ; ; (, IEEE)
    We explore gender bias in the presence of facial masks in automated face recognition systems using various deep learning algorithms in this research study. The paper focuses on an experimental study using an imbalanced image database with a smaller percentage of female subjects compared to a larger percentage of male subjects and examines the impact of masked images in evaluating gender bias. The conducted experiments aim to understand how different algorithms perform in mitigating gender bias in the presence of face masks and highlight the significance of gender distribution within datasets in identifying and mitigating bias. We present the methodology used to conduct the experiments and elaborate the results obtained from male only, female only, and mixed-gender datasets. Overall, this research sheds light on the complexities of gender bias in masked versus unmasked face recognition technology and its implications for real-world applications. 
    more » « less
  3. ; (, IEEE SoutheastCon 2023)
    Automated face recognition is a widely adopted machine learning technology for contactless identification of people in various processes such as automated border control, secure login to electronic devices, community surveillance, tracking school attendance, workplace clock in and clock out. Using face masks have become crucial in our daily life with the recent world-wide COVID-19 pandemic. The use of face masks causes the performance of conventional face recognition technologies to degrade considerably. The effect of mask-wearing in face recognition is yet an understudied issue. In this paper, we address this issue by evaluating the performance of a number of face recognition models which are tested by identifying masked and unmasked face images. We use six conventional machine learning algorithms, which are SVC, KNN, LDA, DT, LR and NB, to find out the ones which perform best, besides the ones which poorly perform, in the presence of masked face images. Local Binary Pattern (LBP) is utilized as the feature extraction operator. We generated and used synthesized masked face images. We prepared unmasked, masked, and half-masked training datasets and evaluated the face recognition performance against both masked and unmasked images to present a broad view of this crucial problem. We believe that our study is unique in elaborating the mask-aware facial recognition with almost all possible scenarios including half_masked-to-masked and half_masked-to-unmasked besides evaluating a larger number of conventional machine learning algorithms compared the other studies in the literature. 
    more » « less
  4. ; ; ; ; ; ; (, Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society)
    We applied techniques from psychology --- typically used to visualize human bias --- to facial analysis systems, providing novel approaches for diagnosing and communicating algorithmic bias. First, we aggregated a diverse corpus of human facial images (N=1492) with self-identified gender and race. We tested four automated gender recognition (AGR) systems and found that some exhibited intersectional gender-by-race biases. Employing a technique developed by psychologists --- face averaging --- we created composite images to visualize these systems' outputs. For example, we visualized what an average woman looks like, according to a system's output. Second, we conducted two online experiments wherein participants judged the bias of hypothetical AGR systems. The first experiment involved participants (N=228) from a convenience sample. When depicting the same results in different formats, facial visualizations communicated bias to the same magnitude as statistics. In the second experiment with only Black participants (N=223), facial visualizations communicated bias significantly more than statistics, suggesting that face averages are meaningful for communicating algorithmic bias. 
    more » « less
  5. ; (, IEEE)
    Automated and contactless face recognition is a widely used machine learning technology for identifying people which has been applied in scenarios like secure login to electronic devices, automated border control, community surveillance, tracking school attendance. The use of face masks has become essential due to the global spread of COVID-19, raising concerns about the performance of recognition systems. Conventional face recognition technologies were primarily designed to work with unmasked faces, and the widespread use of masked face images significantly degrades their performance. To address this understudied issue, we evaluated the performance of six deep learning models, namely, VGG-16, AlexNet, GoogleNet, LeNet, ResNet-50, and FaceNet on masked and unmasked face images. We aim to find out if deep learning models struggle with masked face recognition and identify the models that mitigate the impact of masked face images. We track, and report miss rates for both masked and unmasked images, along with performance metrics like accuracy and F1 scores in this paper. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email