Deep Learning for Face Recognition: Pride or Prejudiced?

By Shruti Nagpal, Maneet Singh, Richa Singh, Mayank Vatsa, Nalini K. Ratha. 2019

83 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Computer Vision and Pattern Recognition, Safety, Robustness & Interpretability

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Citations in the corpus

Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditing (2020, background): “FPT can be deployed for a wide range of uses ranging from smiling detection to gauge customer satisfaction, to estimating the demographic characteristics of a subject population, to tracking individuals using face identification tools [37].”
Face Recognition: Too Bias, or Not Too Bias? (2020, background): “Thus, an effect of the growing dependency on ML is an ever-increasing concern about the biased and unfair algorithms, e . g ., untrustworthy and prejudiced facial recognition (FR) [26, 32].”