Gang Hua

Gang Hua (; born 1979) is a Chinese-American computer scientist who specializes in the field of computer vision and pattern recognition. He is an IEEE Fellow, IAPR Fellow and ACM Distinguished Scientist. He is a key contributor to Microsoft's Facial Recognition technologies. Provided by Wikipedia
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  1. 1
    Computer Vision – ECCV 2016 Workshops Amsterdam, The Netherlands, October 8-10 and 15-16, 2016, Proceedings, Part III /
    Published 2016
    Other Authors: “…Hua, Gang…”
    Full Text via HEAL-Link
    Electronic eBook
  2. 2
    Computer Vision – ECCV 2016 Workshops Amsterdam, The Netherlands, October 8-10 and 15-16, 2016, Proceedings, Part I /
    Published 2016
    Other Authors: “…Hua, Gang…”
    Full Text via HEAL-Link
    Electronic eBook
  3. 3
    Computer Vision – ECCV 2016 Workshops Amsterdam, The Netherlands, October 8-10 and 15-16, 2016, Proceedings, Part II /
    Published 2016
    Other Authors: “…Hua, Gang…”
    Full Text via HEAL-Link
    Electronic eBook
  4. 4
    Mobile Cloud Visual Media Computing From Interaction to Service /
    Published 2015
    Other Authors: “…Hua, Gang…”
    Full Text via HEAL-Link
    Electronic eBook
  5. 5
    Face and Facial Expression Recognition from Real World Videos International Workshop, Stockholm, Sweden, August 24, 2014, Revised Selected Papers /
    Published 2015
    Other Authors: “…Hua, Gang…”
    Full Text via HEAL-Link
    Electronic eBook
  6. 6
    Video Analytics. Face and Facial Expression Recognition and Audience Measurement Third International Workshop, VAAM 2016, and Second International Workshop, FFER 2016, Cancun, Mexico, December 4, 2016, Revised Selected Papers /
    Published 2017
    Other Authors: “…Hua, Gang…”
    Full Text via HEAL-Link
    Electronic eBook
  7. 7
    Video Analytics. Face and Facial Expression Recognition Third International Workshop, FFER 2018, and Second International Workshop, DLPR 2018, Beijing, China, August 20, 2018, Revised Selected Papers /
    Published 2019
    Other Authors: “…Hua, Gang…”
    Full Text via HEAL-Link
    Electronic eBook
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