  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. Secondly, a
distillation process is introduced to maximize the information exploited from
the blackbox face recognition engine. Then a Feature-Conditional Generator
Structure with Exponential Weighting Strategy is presented for a more robust
generator that can synthesize realistic faces with ID preservation. Results on
several benchmarking datasets including CelebA, LFW, AgeDB, CFP-FP against
matching engines have demonstrated the effectiveness of DiBiGAN on both image
realism and ID preservation properties.
