Projects

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    AdaFace: Quality Adaptive Margin for Face Recognition

    Minchul Kim, Anil K. Jain, Xiaoming Liu

    Recognition in low quality face datasets is challenging because facial attributes are obscured and degraded. Advances in margin-based loss functions have resulted in enhanced discriminability of faces in the embedding space. Further, previous studies have studied the effect of adaptive losses to assign more importance to misclassified (hard) examples. In ...

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    Keywords: Face recognition, Biometrics

Publications

2022

  • AdaFace: Quality Adaptive Margin for Face Recognition
    Minchul Kim, Anil K. Jain, Xiaoming Liu
    In Proceeding of IEEE Computer Vision and Pattern Recognition (CVPR 2022), New Orleans, LA, Jun. 2022 (Oral presentation)
    Bibtex | PDF | arXiv | Supplemental | Project Webpage | Code
    • @inproceedings{ adaface-quality-adaptive-margin-for-face-recognition,
      author = { Minchul Kim and Anil K. Jain and Xiaoming Liu },
      title = { AdaFace: Quality Adaptive Margin for Face Recognition },
      booktitle = { In Proceeding of IEEE Computer Vision and Pattern Recognition },
      address = { New Orleans, LA },
      month = { June },
      year = { 2022 },
    }