Vec2Face: Unveil Human Faces from their Blackbox Features in Face Recognition

Understand

Unveiling face images of a subject given his/her high-level representations extracted from a blackbox Face Recognition engine is extremely challenging.

• It is because the limitations of accessible information from that engine including its structure and uninterpretable extracted features.
• This paper presents a novel generative structure with Bijective Metric Learning, namely Bijective Generative Adversarial Networks in a Distillation framework (DiBiGAN), for synthesizing faces of an identity given that person's features.
• In order to effectively address this problem, this work firstly introduces a bijective metric so that the distance measurement and metric learning process can be directly adopted in image domain for an image reconstruction task.