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 movavi video converter. 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 Pop music music? 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 신서유기7. 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 Ulleungdo l. Positively, we obtain the knowledge to improve the current face understanding model toward the next level.