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Face recognition networks encode information about sensitive attributes whilebeing trained for identity classification. Such encoding has two major issues:(a) it makes the face representations susceptible to privacy leakage (b) itappears to contribute to bias in face recognition. However, existing biasmitigation approaches generally require end-to-end training and are unable toachieve high verification accuracy. Therefore, we present a descriptor-basedadversarial de-biasing approach called `Protected Attribute Suppression System(PASS)'. PASS can be trained on top of descriptors obtained from any previouslytrained high-performing network to classify identities and simultaneouslyreduce encoding of sensitive attributes. This eliminates the need forend-to-end training. As a component of PASS, we present a novel discriminatortraining strategy that discourages a network from encoding protected attributeinformation. We show the efficacy of PASS to reduce gender and skintoneinformation in descriptors from SOTA face recognition networks like Arcface. Asa result, PASS descriptors outperform existing baselines in reducing gender andskintone bias on the IJB-C dataset, while maintaining a high verificationaccuracy.

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