Face Embedding Learning

Face Embedding Learning is a machine learning approach in computer vision that belongs to the category of representation learning methods, specifically designed to map facial images into a compact, discriminative latent space where geometric relationships reflect facial identity similarity.

291

Publications

29.1K

Citations

960

Authors

380

Institutions

Publications per year

2017–2026

204

Authors

960

  1. ZW
    Zhongyuan Wang

    Wuhan University, China

    6 publications · 692 citations

    6

    h-index

  2. ND
    Naser Damer

    Fraunhofer Institute for Computer Graphics Research, Germany

    5 publications · 251 citations

    5

    h-index

  3. AB
    Andrew Beng Jin Teoh

    Yonsei University, South Korea

    4 publications · 113 citations

    4

    h-index

  4. ZJ
    Zhong Jin

    Nanjing University of Science and Technology, China

    4 publications · 151 citations

    4

    h-index

  5. FK
    Florian Kirchbuchner

    Fraunhofer Institute for Computer Graphics Research, Germany

    4 publications · 147 citations

    4

    h-index

1–5 of 960

Venues

  1. Pattern Recognition

    30 publications · 2K citations

    22

    h-index

  2. Neurocomputing

    27 publications · 872 citations

    18

    h-index

  3. arXiv (Cornell University)

    Repository · 14 publications · 583 citations

    13

    h-index

  4. Image and Vision Computing

    10 publications · 438 citations

    10

    h-index

  5. International Journal of Computer Vision

    9 publications · 676 citations

    9

    h-index

1–5