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.
翻译:这种编码有两个主要问题:(a) 它使面部表情容易造成隐私泄露;(b) 它似乎在面部识别方面造成偏差;然而,现有的减少偏见办法一般需要端到端培训,无法达到高核实准确性,因此,我们提出了一个称为“保护性禁止制度”的描述性对抗性去偏见办法。PASS可以在从以前受过训练的高级网络获得的描述性文件之上接受培训,以便进行身份分类,同时减少敏感特性的编码。这消除了对端到端培训的需要。作为PASS的一个组成部分,我们提出了一个新的歧视培训战略,阻止网络编码保护性属性信息。我们展示了PASS减少SOTA面识别网络的描述性性别和皮肤信息的效力。结果,PASS的描述性比现有基准要强,以减少IJB-C数据集的性别和皮肤偏差,同时保持高度的准确性。