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Unveiling face images of a subject given his/her high-level representationsextracted from a blackbox Face Recognition engine is extremely challenging. Itis because the limitations of accessible information from that engine includingits structure and uninterpretable extracted features. This paper presents anovel generative structure with Bijective Metric Learning, namely BijectiveGenerative Adversarial Networks in a Distillation framework (DiBiGAN), forsynthesizing faces of an identity given that person's features. In order toeffectively address this problem, this work firstly introduces a bijectivemetric so that the distance measurement and metric learning process can bedirectly adopted in image domain for an image reconstruction task. Secondly, adistillation process is introduced to maximize the information exploited fromthe blackbox face recognition engine. Then a Feature-Conditional GeneratorStructure with Exponential Weighting Strategy is presented for a more robustgenerator that can synthesize realistic faces with ID preservation. Results onseveral benchmarking datasets including CelebA, LFW, AgeDB, CFP-FP againstmatching engines have demonstrated the effectiveness of DiBiGAN on both imagerealism and ID preservation properties.

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