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1. 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. 

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2. Deep Cross Polarimetric Thermal-to- visible Face Recognition

   Iranmanesh, Seyed-Mehdi; Nasrabadi, Nasser M (February 2018, The 11th IAPR International Conference on Biometrics (ICB 2018))

   In this paper, we present a deep coupled learning framework to address the problem of matching polarimetric thermal face photos against a gallery of visible faces. Polarization state information of thermal faces provides the missing textural and geometrics details in the thermal face imagery which exist in visible spectrum. we propose a coupled deep neural network architecture which leverages relatively large visible and thermal datasets to overcome the problem of overfitting and eventually we train it by a polarimetric thermal face dataset which is the first of its kind. The proposed architecture is able to make full use of the polarimetric thermal information to train a deep model compared to the conventional shallow thermal-to-visible face recognition methods. Proposed coupled deep neural network also finds global discriminative features in a nonlinear embedding space to relate the polarimetric thermal faces to their corresponding visible faces. The results show the superiority of our method compared to the state-of-the-art models in cross thermal-to-visible face recognition algorithms. 

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3. Evaluating Impact of Wearing Masks in Face Recognition Using Deep Learning Algorithms

   https://doi.org/10.1109/ICMLA58977.2023.00217  

   Atay, Mustafa; Poudyel, Megh (December 2023, 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. 

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4. Fast Geometrically-Perturbed Adversarial Faces

   https://doi.org/10.1109/WACV.2019.00215  

   Dabouei, Ali; Soleymani, Sobhan; Dawson, Jeremy; Nasrabadi, Nasser (January 2019, IEEE Winter Conference on Applications of Computer Vision (WACV))

   The state-of-the-art performance of deep learning algorithms has led to a considerable increase in the utilization of machine learning in security-sensitive and critical applications. However, it has recently been shown that a small and carefully crafted perturbation in the input space can completely fool a deep model. In this study, we explore the extent to which face recognition systems are vulnerable to geometrically-perturbed adversarial faces. We propose a fast landmark manipulation method for generating adversarial faces, which is approximately 200 times faster than the previous geometric attacks and obtains 99.86% success rate on the state-of-the-art face recognition models. To further force the generated samples to be natural, we introduce a second attack constrained on the semantic structure of the face which has the half speed of the first attack with the success rate of 99.96%. Both attacks are extremely robust against the state-of-the-art defense methods with the success rate of equal or greater than 53.59%. Code is available at https://github.com/alldbi/FLM. 

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5. Imitation-Projected Programmatic Reinforcement Learning

   Verma, A (December 2019, Advances in neural information processing systems)

   Wallach, H (Ed.)

   We study the problem of programmatic reinforcement learning, in which policies are represented as short programs in a symbolic language. Programmatic policies can be more interpretable, generalizable, and amenable to formal verification than neural policies; however, designing rigorous learning approaches for such policies remains a challenge. Our approach to this challenge-a meta-algorithm called PROPEL-is based on three insights. First, we view our learning task as optimization in policy space, modulo the constraint that the desired policy has a programmatic representation, and solve this optimization problem using a form of mirror descent that takes a gradient step into the unconstrained policy space and then projects back onto the constrained space. Second, we view the unconstrained policy space as mixing neural and programmatic representations, which enables employing state-of-the-art deep policy gradient approaches. Third, we cast the projection step as program synthesis via imitation learning, and exploit contemporary combinatorial methods for this task. We present theoretical convergence results for PROPEL and empirically evaluate the approach in three continuous control domains. The experiments show that PROPEL can significantly outperform state-of-the-art approaches for learning programmatic policies. 

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