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OneFace: One Threshold for All

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13672))

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Abstract

Face recognition (FR) has witnessed remarkable progress with the surge of deep learning. Current FR evaluation protocols usually adopt different thresholds to calculate the True Accept Rate (TAR) under a pre-defined False Accept Rate (FAR) for different datasets. However, in practice, when the FR model is deployed on industry systems (e.g., hardware devices), only one fixed threshold is adopted for all scenarios to distinguish whether a face image pair belongs to the same identity. Therefore, current evaluation protocols using different thresholds for different datasets are not fully compatible with the practical evaluation scenarios with one fixed threshold, and it is critical to measure the performance of FR models by using one threshold for all datasets. In this paper, we rethink the limitations of existing evaluation protocols for FR and propose to evaluate the performance of FR models from a new perspective. Specifically, in our OneFace, we first propose the One-Threshold-for-All (OTA) evaluation protocol for FR, which utilizes one fixed threshold called as Calibration Threshold to measure the performance on different datasets. Then, to improve the performance of FR models under the OTA protocol, we propose the Threshold Consistency Penalty (TCP) to improve the consistency of the thresholds among multiple domains, which includes Implicit Domain Division (IDD) as well as Calibration and Domain Thresholds Estimation (CDTE). Extensive experimental results demonstrate the effectiveness of our method for FR.

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Acknowledgments

This research was supported by National Natural Science Foundation of China under Grant 61932002.

Author information

Authors and Affiliations

  1. State Key Lab of Software Development Environment, Beihang University, Beijing, China

    Jiaheng Liu & Ke Xu

  2. University of Chinese Academy of Sciences, Beijing, China

    Zhipeng Yu

  3. SenseTime Group Limited, Science Park, Hong Kong

    Haoyu Qin, Yichao Wu & Ding Liang

  4. The University of Hong Kong, Pok Fu Lam, Hong Kong

    Gangming Zhao

Authors
  1. Jiaheng Liu
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  2. Zhipeng Yu
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  3. Haoyu Qin
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  4. Yichao Wu
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  5. Ding Liang
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  6. Gangming Zhao
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  7. Ke Xu
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Corresponding author

Correspondence to Zhipeng Yu .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Liu, J. et al. (2022). OneFace: One Threshold for All. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13672. Springer, Cham. https://doi.org/10.1007/978-3-031-19775-8_32

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  • DOI: https://doi.org/10.1007/978-3-031-19775-8_32

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-031-19775-8

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