More Like this

1. An Exploratory Study of Masked Face Recognition with Machine Learning Algorithms

   https://doi.org/10.1109/SoutheastCon51012.2023.10115205  

   Pudyel, Megh; Atay, Mustafa (April 2023, 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
2. Evaluation of Gender Bias in Masked Face Recognition with Deep Learning Models

   https://doi.org/10.1109/SSCI52147.2023.10371811  

   Atay, Mustafa; Poudyel, Megh; Evora, Saul (December 2023, 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. Masked Face Analysis via Multi-Task Deep Learning

   https://doi.org/10.3390/jimaging7100204  

   Patel, Vatsa S.; Nie, Zhongliang; Le, Trung-Nghia; Nguyen, Tam V. (October 2021, Journal of Imaging)

   Face recognition with wearable items has been a challenging task in computer vision and involves the problem of identifying humans wearing a face mask. Masked face analysis via multi-task learning could effectively improve performance in many fields of face analysis. In this paper, we propose a unified framework for predicting the age, gender, and emotions of people wearing face masks. We first construct FGNET-MASK, a masked face dataset for the problem. Then, we propose a multi-task deep learning model to tackle the problem. In particular, the multi-task deep learning model takes the data as inputs and shares their weight to yield predictions of age, expression, and gender for the masked face. Through extensive experiments, the proposed framework has been found to provide a better performance than other existing methods. 

   more » « less
4. What Is the Challenge for Deep Learning in Unconstrained Face Recognition?

   https://doi.org/10.1109/FG.2018.00070  

   Guo, Guodong; Zhang, Na (May 2018, IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018))

   Recently deep learning has become dominant in face recognition and many other artificial intelligence areas. We raise a question: Can deep learning truly solve the face recognition problem? If not, what is the challenge for deep learning methods in face recognition? We think that the face image quality issue might be one of the challenges for deep learning, especially in unconstrained face recognition. To investigate the problem, we partition face images into different qualities, and evaluate the recognition performance, using the state-of-the-art deep networks. Some interesting results are obtained, and our studies can show directions to promote the deep learning methods towards high-accuracy and practical use in solving the hard problem of unconstrained face recognition. 

   more » « less
5. Evaluation of Gender Bias in Facial Recognition with Traditional Machine Learning Algorithms

   https://doi.org/10.1109/SSCI50451.2021.9660186  

   Atay, Mustafa; Gipson, Hailey; Gwyn, Tony; Roy, Kaushik (December 2021, IEEE Symposium Series on Computational Intelligence (SSCI))

   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