TY - GEN
T1 - Face classification
T2 - 11th Chinese Conference on Biometric Recognition, CCBR 2016
AU - Duan, Jiali
AU - Liao, Shengcai
AU - Zhou, Shuai
AU - Li, Stan Z.
N1 - Publisher Copyright:
© Springer International Publishing AG 2016.
PY - 2016
Y1 - 2016
N2 - Face detection evaluation generally involves three steps: block generation, face classification, and post-processing. However, firstly, face detection performance is largely influenced by block generation and post-processing, concealing the performance of face classification core module. Secondly, implementing and optimizing all the three steps results in a very heavy work, which is a big barrier for researchers who only cares about classification. Motivated by this, we conduct a specialized benchmark study in this paper, which focuses purely on face classification. We start with face proposals, and build a benchmark dataset with about 3.5 million patches for two-class face/non-face classification. Results with several baseline algorithms show that, without the help of post-processing, the performance of face classification itself is still not very satisfactory, even with a powerful CNN method. We’ll release this benchmark to help assess performance of face classification only, and ease the participation of other related researchers.
AB - Face detection evaluation generally involves three steps: block generation, face classification, and post-processing. However, firstly, face detection performance is largely influenced by block generation and post-processing, concealing the performance of face classification core module. Secondly, implementing and optimizing all the three steps results in a very heavy work, which is a big barrier for researchers who only cares about classification. Motivated by this, we conduct a specialized benchmark study in this paper, which focuses purely on face classification. We start with face proposals, and build a benchmark dataset with about 3.5 million patches for two-class face/non-face classification. Results with several baseline algorithms show that, without the help of post-processing, the performance of face classification itself is still not very satisfactory, even with a powerful CNN method. We’ll release this benchmark to help assess performance of face classification only, and ease the participation of other related researchers.
KW - Benchmark evaluation
KW - Face classification
KW - Face detection
UR - http://www.scopus.com/inward/record.url?scp=84992456182&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84992456182&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-46654-5_3
DO - 10.1007/978-3-319-46654-5_3
M3 - Conference contribution
AN - SCOPUS:84992456182
SN - 9783319466538
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 22
EP - 29
BT - Biometric Recognition - 11th Chinese Conference, CCBR 2016, Proceedings
A2 - Shan, Shiguang
A2 - You, Zhisheng
A2 - Zhou, Jie
A2 - Zheng, Weishi
A2 - Wang, Yunhong
A2 - Sun, Zhenan
A2 - Feng, Jianjiang
A2 - Zhao, Qijun
PB - Springer Verlag
Y2 - 14 October 2016 through 16 October 2016
ER -