Deep Learning for Face Recognition: Pride or Prejudiced?

Abstract

Do very high accuracies of deep networks suggest pride of effective AI or are deep networks prejudiced? Do they suffer from in-group biases (own-race-bias and own-age-bias), and mimic the human behavior? Is in-group specific information being encoded sub-consciously by the deep networks? This research attempts to answer these questions and presents an in-depth analysis of `bias' in deep learning based face recognition systems. This is the first work which decodes if and where bias is encoded for face recognition. Taking cues from cognitive studies, we inspect if deep networks are also affected by social in- and out-group effect. Networks are analyzed for own-race and own-age bias, both of which have been well established in human beings. The sub-conscious behavior of face recognition models is examined to understand if they encode race or age specific features for face recognition. Analysis is performed based on 36 experiments conducted on multiple datasets. Four deep learning networks either trained from scratch or pre-trained on over 10M images are used. Variations across class activation maps and feature visualizations provide novel insights into the functioning of deep learning systems, suggesting behavior similar to humans. It is our belief that a better understanding of state-of-the-art deep learning networks would enable researchers to address the given challenge of bias in AI, and develop fairer systems.

References in corpus (2)

• Women also Snowboard: Overcoming Bias in Captioning Models
• InclusiveFaceNet: Improving Face Attribute Detection with Race and Gender Diversity

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• Fairness in Deep Learning: A Computational Perspective
• CASIA-Face-Africa: A Large-scale African Face Image Database
• A Set of Distinct Facial Traits Learned by Machines Is Not Predictive of Appearance Bias in the Wild
• Exploring Biases and Prejudice of Facial Synthesis via Semantic Latent Space
• Automatic Face Understanding: Recognizing Families in Photos
• Harnessing Geometric Constraints from Emotion Labels to improve Face Verification