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

Authors

7.9K

  1. SS
    Shiguang Shan

    Institute of Computing Technology, China

    60 publications · 7.9K citations

    39

    h-index

  2. XC
    Xilin Chen

    Institute of Computing Technology, China

    50 publications · 7.1K citations

    33

    h-index

  3. RH
    Ran He

    Institute of Automation, China

    39 publications · 4.5K citations

    29

    h-index

  4. JL
    Jiwen Lu

    Tsinghua University, China

    36 publications · 3.2K citations

    29

    h-index

  5. XT
    Xiaoou Tang

    Chinese University of Hong Kong, Hong Kong SAR China

    34 publications · 17.5K citations

    29

    h-index

1–5 of 7.9K

Venues

  1. Pattern Recognition

    178 publications · 11.2K citations

    60

    h-index

  2. IEEE Transactions on Pattern Analysis and Machine Intelligence

    TPAMI · 74 publications · 16.2K citations

    51

    h-index

  3. arXiv (Cornell University)

    Repository · 252 publications · 12.6K citations

    48

    h-index

  4. IEEE Transactions on Image Processing

    100 publications · 8.6K citations

    46

    h-index

  5. Neurocomputing

    152 publications · 7K citations

    41

    h-index

1–5