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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 2: AAAI-22 Technical Tracks 2

AnchorFace: Boosting TAR@FAR for Practical Face Recognition

February 1, 2023

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Authors

Jiaheng Liu

Beihang University


Haoyu Qin

SenseTime Group Limited


Yichao Wu

SenseTime Group Limited


Ding Liang

SenseTime Group Limited


DOI:

10.1609/aaai.v36i2.20063


Abstract:

Within the field of face recognition (FR), it is widely accepted that the key objective is to optimize the entire feature space in the training process and acquire robust feature representations. However, most real-world FR systems tend to operate at a pre-defined False Accept Rate (FAR), and the corresponding True Accept Rate (TAR) represents the performance of the FR systems, which indicates that the optimization on the pre-defined FAR is more meaningful and important in the practical evaluation process. In this paper, we call the predefined FAR as Anchor FAR, and we argue that the existing FR loss functions cannot guarantee the optimal TAR under the Anchor FAR, which impedes further improvements of FR systems. To this end, we propose AnchorFace to bridge the aforementioned gap between the training and practical evaluation process for FR. Given the Anchor FAR, AnchorFace can boost the performance of FR systems by directly optimizing the non-differentiable FR evaluation metrics. Specifically, in AnchorFace, we first calculate the similarities of the positive and negative pairs based on both the features of the current batch and the stored features in the maintained online-updating set. Then, we generate the differentiable TAR loss and FAR loss using a soften strategy. Our AnchorFace can be readily integrated into most existing FR loss functions, and extensive experimental results on multiple benchmark datasets demonstrate the effectiveness of AnchorFace.

Topics: AAAI

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HOW TO CITE:

Jiaheng Liu||Haoyu Qin||Yichao Wu||Ding Liang AnchorFace: Boosting TAR@FAR for Practical Face Recognition Proceedings of the AAAI Conference on Artificial Intelligence (2022) 1711-1719.

Jiaheng Liu||Haoyu Qin||Yichao Wu||Ding Liang AnchorFace: Boosting TAR@FAR for Practical Face Recognition AAAI 2022, 1711-1719.

Jiaheng Liu||Haoyu Qin||Yichao Wu||Ding Liang (2022). AnchorFace: Boosting TAR@FAR for Practical Face Recognition. Proceedings of the AAAI Conference on Artificial Intelligence, 1711-1719.

Jiaheng Liu||Haoyu Qin||Yichao Wu||Ding Liang. AnchorFace: Boosting TAR@FAR for Practical Face Recognition. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.1711-1719.

Jiaheng Liu||Haoyu Qin||Yichao Wu||Ding Liang. 2022. AnchorFace: Boosting TAR@FAR for Practical Face Recognition. "Proceedings of the AAAI Conference on Artificial Intelligence". 1711-1719.

Jiaheng Liu||Haoyu Qin||Yichao Wu||Ding Liang. (2022) "AnchorFace: Boosting TAR@FAR for Practical Face Recognition", Proceedings of the AAAI Conference on Artificial Intelligence, p.1711-1719

Jiaheng Liu||Haoyu Qin||Yichao Wu||Ding Liang, "AnchorFace: Boosting TAR@FAR for Practical Face Recognition", AAAI, p.1711-1719, 2022.

Jiaheng Liu||Haoyu Qin||Yichao Wu||Ding Liang. "AnchorFace: Boosting TAR@FAR for Practical Face Recognition". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.1711-1719.

Jiaheng Liu||Haoyu Qin||Yichao Wu||Ding Liang. "AnchorFace: Boosting TAR@FAR for Practical Face Recognition". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 1711-1719.

Jiaheng Liu||Haoyu Qin||Yichao Wu||Ding Liang. AnchorFace: Boosting TAR@FAR for Practical Face Recognition. AAAI[Internet]. 2022[cited 2023]; 1711-1719.


ISSN: 2374-3468


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