TY - GEN
T1 - Deep metric learning with improved triplet loss for face clustering in videos
AU - Zhang, Shun
AU - Gong, Yihong
AU - Wang, Jinjun
N1 - Publisher Copyright:
© Springer International Publishing AG 2016.
PY - 2016
Y1 - 2016
N2 - Face clustering in videos is to partition a large amount of faces into a given number of clusters, such that some measure of distance is minimized within clusters and maximized between clusters. In real-world videos, head pose, facial expression, scale, illumination, occlusion and some uncontrolled factors may dramatically change the appearance variations of faces. In this paper, we tackle this problem by learning non-linear metric function with a deep convolutional neural network from the input image to a low-dimensional feature embedding with the visual constraints among face tracks. Our network directly optimizes the embedding space so that the Euclidean distances correspond to a measure of semantic face similarity. This is technically realized by minimizing an improved triplet loss function, which pushes the negative face away from the positive pairs, and requires the distance of the positive pair to be less than a margin. We extensively evaluate the proposed algorithm on a set of challenging videos and demonstrate significant performance improvement over existing techniques.
AB - Face clustering in videos is to partition a large amount of faces into a given number of clusters, such that some measure of distance is minimized within clusters and maximized between clusters. In real-world videos, head pose, facial expression, scale, illumination, occlusion and some uncontrolled factors may dramatically change the appearance variations of faces. In this paper, we tackle this problem by learning non-linear metric function with a deep convolutional neural network from the input image to a low-dimensional feature embedding with the visual constraints among face tracks. Our network directly optimizes the embedding space so that the Euclidean distances correspond to a measure of semantic face similarity. This is technically realized by minimizing an improved triplet loss function, which pushes the negative face away from the positive pairs, and requires the distance of the positive pair to be less than a margin. We extensively evaluate the proposed algorithm on a set of challenging videos and demonstrate significant performance improvement over existing techniques.
KW - Deep metric learning
KW - Face clustering in videos
KW - Improved triple loss
UR - https://www.scopus.com/pages/publications/85007236696
U2 - 10.1007/978-3-319-48890-5_49
DO - 10.1007/978-3-319-48890-5_49
M3 - 会议稿件
AN - SCOPUS:85007236696
SN - 9783319488899
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 497
EP - 508
BT - Advances in Multimedia Information Processing – 17th Pacific-Rim Conference on Multimedia, PCM 2016, Proceedings
A2 - Chen, Enqing
A2 - Tie, Yun
A2 - Gong, Yihong
PB - Springer Verlag
T2 - 17th Pacific-Rim Conference on Multimedia, PCM 2016
Y2 - 15 September 2016 through 16 September 2016
ER -