Ryota Natsume*, Kazuki Inoue*, Yoshihiro Fukuhara, Shintaro Yamamoto, Shigeo Morishima, Hirokatsu Kataoka (*equal contribution)
Brain-Driven Computer Vision Workshop (BDCV 2018) in ECCV 2018, Posters
The research of face is one of the most active topics in computer vision, and deep neural networks (DNNs) fill the gap between human-level and computer-driven performance in face verification. The performance gap seems to get close in the accuracy-based expectation. However, a curious scenario appears, namely face understanding is really close to the human-level performance? In the paper, to confirm the brain-driven concept, we conduct image-based detection, classification and generation on our Fake-Face database. The Fake-Face database has two configurations: (i) false positives by face detector (e.g. VJ, CNNs), and (ii) Simulacra faces which seem to be human faces but there is no real-face. The result shows a suggestive knowledge, such as the recent vision-based face understanding still remains the gap from the human-level performance. Positively, we obtain the knowledge to improve the current face understanding model toward the next level.