Face Representation Learning
Face Representation Learning is a machine learning methodology that specifically focuses on extracting discriminative and invariant facial features from raw pixel data to form compact, meaningful representations suitable for face recognition tasks.
3.4K
Publications
289.5K
Citations
7.9K
Authors
1.7K
Institutions
Publications per year
2017–2026
2.1K
Parents
3.4K
FaceNet: A unified embedding for face recognition and clustering
2015 · 11.2K citations · Full text
Deep Learning Face Attributes in the Wild
Ziwei Liu, Ping Luo, Xiaogang Wang et al. · 2015 · 7.7K citations
DeepFace: Closing the Gap to Human-Level Performance in Face Verification
Yaniv Taigman, Ming Yang, Marc’Aurelio Ranzato et al. · 2014 · 6.6K citations
1–5 of 3.4K
7.9K
39
h-index
33
h-index
29
h-index
29
h-index
29
h-index
1–5 of 7.9K
1.7K

Chinese Academy of Sciences
Beijing, China
72 h-index
525 publications
49.9K citations
University of Chinese Academy of Sciences
Beijing, China
54 h-index
320 publications
28.2K citations
Tsinghua University
Beijing, China
44 h-index
199 publications
15.8K citations
Chinese University of Hong Kong
Hong Kong, Hong Kong
41 h-index
155 publications
63.7K citations
Michigan State University
East Lansing, United States
38 h-index
111 publications
15.8K citations

Harbin Institute of Technology
Harbin, China
38 h-index
114 publications
12.9K citations
Institute of Automation
Beijing, China
37 h-index
198 publications
18.8K citations
Nanjing University of Science and Technology
Nanjing, China
36 h-index
167 publications
9.9K citations
1–8 of 1.7K
178 publications · 11.2K citations
60
h-index
TPAMI · 74 publications · 16.2K citations
51
h-index
Repository · 252 publications · 12.6K citations
48
h-index
100 publications · 8.6K citations
46
h-index
152 publications · 7K citations
41
h-index
1–5