TY - GEN
T1 - Understanding fake faces
AU - Natsume, Ryota
AU - Inoue, Kazuki
AU - Fukuhara, Yoshihiro
AU - Yamamoto, Shintaro
AU - Morishima, Shigeo
AU - Kataoka, Hirokatsu
N1 - Funding Information:
Acknowledgments. This study was granted in part by the Strategic Basic Research Program ACCEL of the Japan Science and Technology Agency (JPMJAC1602). Shigeo Morishima was supported by a Grant-in-Aid from Waseda Institute of Advanced Science and Engineering. We have had the support and encouragement of cvpa-per.challenege group.
Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - Face recognition research is one of the most active topics in computer vision (CV), and deep neural networks (DNN) are now filling the gap between human-level and computer-driven performance levels in face verification algorithms. However, although the performance gap appears to be narrowing in terms of accuracy-based expectations, a curious question has arisen; specifically, Face understanding of AI is really close to that of human? In the present study, in an effort to confirm the brain-driven concept, we conduct image-based detection, classification, and generation using an in-house created fake face database. This database has two configurations: (i) false positive face detections produced using both the Viola Jones (VJ) method and convolutional neural networks (CNN), and (ii) simulacra that have fundamental characteristics that resemble faces but are completely artificial. The results show a level of suggestive knowledge that indicates the continuing existence of a gap between the capabilities of recent vision-based face recognition algorithms and human-level performance. On a positive note, however, we have obtained knowledge that will advance the progress of face-understanding models.
AB - Face recognition research is one of the most active topics in computer vision (CV), and deep neural networks (DNN) are now filling the gap between human-level and computer-driven performance levels in face verification algorithms. However, although the performance gap appears to be narrowing in terms of accuracy-based expectations, a curious question has arisen; specifically, Face understanding of AI is really close to that of human? In the present study, in an effort to confirm the brain-driven concept, we conduct image-based detection, classification, and generation using an in-house created fake face database. This database has two configurations: (i) false positive face detections produced using both the Viola Jones (VJ) method and convolutional neural networks (CNN), and (ii) simulacra that have fundamental characteristics that resemble faces but are completely artificial. The results show a level of suggestive knowledge that indicates the continuing existence of a gap between the capabilities of recent vision-based face recognition algorithms and human-level performance. On a positive note, however, we have obtained knowledge that will advance the progress of face-understanding models.
KW - Face recognition
KW - False positives
KW - Simulacra
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U2 - 10.1007/978-3-030-11015-4_42
DO - 10.1007/978-3-030-11015-4_42
M3 - Conference contribution
AN - SCOPUS:85061694140
SN - 9783030110147
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 566
EP - 576
BT - Computer Vision – ECCV 2018 Workshops, Proceedings
A2 - Leal-Taixé, Laura
A2 - Roth, Stefan
PB - Springer Verlag
T2 - 15th European Conference on Computer Vision, ECCV 2018
Y2 - 8 September 2018 through 14 September 2018
ER -