Mitigating Bias in Face Recognition Using Skewness-Aware Reinforcement Learning

Wang, Mei; Deng, Weihong

doi:10.1109/cvpr42600.2020.00934

Cited by 180 publications

(138 citation statements)

References 25 publications

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“…It originated from humans' decision-making procedure [30], enabling the agent to decide the behavior from its experiences by trial and error. Figure 9 In RL-RBN [32], the racial bias of face recognition has been reduced. The authors also proposed an optimal margin loss for this model.…”

Section: F Deep Reinforcement Learningmentioning

confidence: 99%

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Recent Advances in Deep Learning Techniques for Face Recognition

et al. 2021

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In recent years, researchers have proposed many deep learning (DL) methods for various tasks, and particularly face recognition (FR) made an enormous leap using these techniques. Deep FR systems benefit from the hierarchical architecture of the DL methods to learn discriminative face representation. Therefore, DL techniques significantly improve state-of-the-art performance on FR systems and encourage diverse and efficient real-world applications. In this paper, we present a comprehensive analysis of various FR systems that leverage the different types of DL techniques, and for the study, we summarize 171 recent contributions from this area. We discuss the papers related to different algorithms, architectures, loss functions, activation functions, datasets, challenges, improvement ideas, current and future trends of DL-based FR systems. We provide a detailed discussion of various DL methods to understand the current state-of-the-art, and then we discuss various activation and loss functions for the methods. Additionally, we summarize different datasets used widely for FR tasks and discuss challenges related to illumination, expression, pose variations, and occlusion. Finally, we discuss improvement ideas, current and future trends of FR tasks. INDEX TERMSDeep learning, face recognition, artificial neural network, convolutional neural network, auto encoder, generative adversarial network, deep belief network, reinforcement learning.

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Section: F Deep Reinforcement Learningmentioning

confidence: 99%

“…It is used as a part of a hybrid method like CNN and RL or GAN and RL. The researchers use RL to solve some problems, for instance, adaptation of loss functions [31], skewness embedding [32], user authentication [33], and searching a set of dominant features [34].…”

Section: F Deep Reinforcement Learningmentioning

confidence: 99%

Recent Advances in Deep Learning Techniques for Face Recognition

et al. 2021

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“…They proposed Cluster-based Large Margin Local Embedding (CLMLE) method, which maintains inter-cluster margin among the same and different classes. Wang and Deng (2019) proposed a reinforcement learning-based race balance network (RL-RBN) to mitigate racial bias. Singh et al (2020) provided a review of techniques related to bias in face recognition.…”

Section: Related Workmentioning

confidence: 99%

Subgroup Invariant Perturbation for Unbiased Pre-Trained Model Prediction

et al. 2021

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Modern deep learning systems have achieved unparalleled success and several applications have significantly benefited due to these technological advancements. However, these systems have also shown vulnerabilities with strong implications on the fairness and trustability of such systems. Among these vulnerabilities, bias has been an Achilles’ heel problem. Many applications such as face recognition and language translation have shown high levels of bias in the systems towards particular demographic sub-groups. Unbalanced representation of these sub-groups in the training data is one of the primary reasons of biased behavior. To address this important challenge, we propose a two-fold contribution: a bias estimation metric termed as Precise Subgroup Equivalence to jointly measure the bias in model prediction and the overall model performance. Secondly, we propose a novel bias mitigation algorithm which is inspired from adversarial perturbation and uses the PSE metric. The mitigation algorithm learns a single uniform perturbation termed as Subgroup Invariant Perturbation which is added to the input dataset to generate a transformed dataset. The transformed dataset, when given as input to the pre-trained model reduces the bias in model prediction. Multiple experiments performed on four publicly available face datasets showcase the effectiveness of the proposed algorithm for race and gender prediction.

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“…Racial Faces in-the-Wild (RFW) [32], [33], [34] was collected from MS-Celeb-1M, and have four labels in which, each label contains 10K images of 3K subjects [32]. RFW is similar to the Arab dataset in terms of data source (internet).…”

Section: A Datasetsmentioning

confidence: 99%

A Classification of Arab Ethnicity Based on Face Image Using Deep Learning Approach

And

Prince

2021

IEEE Access

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Human face and facial features gain a lot of attention from researchers and are considered as one of the most popular topics recently. Features and information extracted from a person are known as soft biometric, they have been used to improve the recognition performance and enhance the search engine for face images, which can be further applied in various fields such as law enforcement, surveillance videos, advertisement, and social media profiling. By observing relevant studies in the field, we noted a lack of mention of the Arab world and an absence of Arab dataset as well. Therefore, our aim in this paper is to create an Arab dataset with proper labeling of Arab sub-ethnic groups, then classify these labels using deep learning approaches. Arab image dataset that was created consists of three labels: Gulf Cooperation Council countries (GCC), the Levant, and Egyptian. Two types of learning were used to solve the problem. The first type is supervised deep learning (classification); a Convolutional Neural Network (CNN) pre-trained model has been used as CNN models achieved state of art results in computer vision classification problems. The second type is unsupervised deep learning (deep clustering). The aim of using unsupervised learning is to explore the ability of such models in classifying ethnicities. To our knowledge, this is the first time deep clustering is used for ethnicity classification problems. For this, three methods were chosen. The best result of training a pre-trained CNN on the full Arab dataset then evaluating on a different dataset was 56.97%, and 52.12% when Arab dataset labels were balanced. The methods of deep clustering were applied on different datasets, showed an ACC from 32% to 59%, and NMI and ARI result from zero to 0.2714 and 0.2543 respectively.

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Mitigating Bias in Face Recognition Using Skewness-Aware Reinforcement Learning

Cited by 180 publications

References 25 publications

Recent Advances in Deep Learning Techniques for Face Recognition

Recent Advances in Deep Learning Techniques for Face Recognition

Subgroup Invariant Perturbation for Unbiased Pre-Trained Model Prediction

A Classification of Arab Ethnicity Based on Face Image Using Deep Learning Approach

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