PASS: Protected Attribute Suppression System for Mitigating Bias in Face Recognition

• URL: http://arxiv.org/abs/2108.03764v1
• Date: Mon, 9 Aug 2021 00:39:22 GMT
• Title: PASS: Protected Attribute Suppression System for Mitigating Bias in Face Recognition
• Authors: Prithviraj Dhar, Joshua Gleason, Aniket Roy, Carlos D. Castillo, Rama Chellappa
• Abstract summary: Face recognition networks encode information about sensitive attributes while being trained for identity classification. Existing bias mitigation approaches require end-to-end training and are unable to achieve high verification accuracy. We present a descriptors-based adversarial de-biasing approach called Protected Attribute Suppression System ( PASS)' Pass can be trained on top of descriptors obtained from any previously trained high-performing network to classify identities and simultaneously reduce encoding of sensitive attributes.
• Score: 55.858374644761525
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Face recognition networks encode information about sensitive attributes while being trained for identity classification. Such encoding has two major issues: (a) it makes the face representations susceptible to privacy leakage (b) it appears to contribute to bias in face recognition. However, existing bias mitigation approaches generally require end-to-end training and are unable to achieve high verification accuracy. Therefore, we present a descriptor-based adversarial de-biasing approach called `Protected Attribute Suppression System (PASS)'. PASS can be trained on top of descriptors obtained from any previously trained high-performing network to classify identities and simultaneously reduce encoding of sensitive attributes. This eliminates the need for end-to-end training. As a component of PASS, we present a novel discriminator training strategy that discourages a network from encoding protected attribute information. We show the efficacy of PASS to reduce gender and skintone information in descriptors from SOTA face recognition networks like Arcface. As a result, PASS descriptors outperform existing baselines in reducing gender and skintone bias on the IJB-C dataset, while maintaining a high verification accuracy.

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