The contribution of different face parts to deep face recognition

Abstract

The development of face recognition improvements still lacks knowledge on what parts of the face are important. In this article, the authors present face parts analysis to obtain important recognition information in a certain area of the face, more than just the eye or eyebrow, from the black box perspective. In addition, the authors propose a more advanced way to select parts without introducing artifacts using the average face and morphing. Furthermore, multiple face recognition systems are used to analyze the face component contribution. Finally, the results show that the four deep face recognition systems produce a different behavior for each experiment. However, the eyebrows are still the most important part of deep face recognition systems. In addition, the face texture played an important role deeper than the face shape.

E2f-Net: Eyes-to-Face Inpainting Via Stylegan Latent Space

“…Experiments are conducted on seven generated datasets: E2F-StyleGANdb, E2F-CelebA-HQ, E2F-FFHQ, E2F-MS1MV2, E2F-LFW, E2F-CFP-FP, E2F-AgeDB-30; which are all available on the project's webpage. The images are resized to 256 × 256, and then a landmark detector [53] is used to locate and clip the eyes in order to extract the periocular area from each facial image.…”