Your Face, Your Privacy: Combating Unauthorized Usage

Atul Kumar, Akshay Agarwal, Nalini K. Ratha

Abstract: The high performance of current deep face recognition systems and their unauthorized usage have raised a severe concern for privacy in the physical, adversarial, and digital domains. To protect privacy, users are exploring several ways, and one such method that recently gained attention is individuals deliberately obscuring their faces with their hands, presumably to avoid facial recognition technology. Since deep face recognition algorithms can handle partial tampering of faces, this raises a critical question of whether these deliberate attempts can protect privacy. In the literature, no evaluation exists that showcases that this type of hiding can bypass the face recognition algorithms. Therefore, in this first-ever study, we have performed extensive research by first developing multiple nose and mouth occlusion datasets using synthetic patches and real-life objects. Our extensive experimentation reveals several interesting observations reflecting the fact that even when a patch is a face patch extracted from an unseen subject, it can fool the face recognition networks. Further, not only face recognition networks, but also it is observed that the proposed patches are effective in deceiving the soft biometric classifier, i.e., the classifier detecting the gender and ethnicity of individuals.