论文标题
朝性别中性的面部描述符，以减轻面部识别的偏见
Towards Gender-Neutral Face Descriptors for Mitigating Bias in Face Recognition
论文作者
论文摘要
最新的深层网络在接受面部识别的培训时隐含地编码性别信息。性别通常被视为与识别面孔有关的重要属性。但是，面部描述符中性别信息的隐式编码有两个主要问题：（a。）使描述符容易受到隐私泄漏的影响，即可以培训恶意药物来预测此类描述符的面部性别。 （b。）它似乎导致了面部识别的性别偏见，即我们发现雄性和女性面孔的DCNN识别准确性有显着差异。因此，我们提出了一种新颖的“对抗性性别偏见算法（议程）”，以减少从先前训练的面部识别网络获得的面部描述中存在的性别信息。我们表明，议程大大降低了面部描述符的性别可预测性。因此，我们还能够减少面部验证中的性别偏见，同时保持合理的识别绩效。
State-of-the-art deep networks implicitly encode gender information while being trained for face recognition. Gender is often viewed as an important attribute with respect to identifying faces. However, the implicit encoding of gender information in face descriptors has two major issues: (a.) It makes the descriptors susceptible to privacy leakage, i.e. a malicious agent can be trained to predict the face gender from such descriptors. (b.) It appears to contribute to gender bias in face recognition, i.e. we find a significant difference in the recognition accuracy of DCNNs on male and female faces. Therefore, we present a novel `Adversarial Gender De-biasing algorithm (AGENDA)' to reduce the gender information present in face descriptors obtained from previously trained face recognition networks. We show that AGENDA significantly reduces gender predictability of face descriptors. Consequently, we are also able to reduce gender bias in face verification while maintaining reasonable recognition performance.
