超越解耦的集合人脸识别：基于方差词汇的突发性抑制
Set-Based Face Recognition Beyond Disentanglement: Burstiness Suppression With Variance Vocabulary
AI总结：
本文针对集合人脸识别中的质量与突发性问题，提出轻量级解耦框架分离身份与方差特征，并设计基于词汇的突发抑制方法VBS，在SFR基准上取得新最优性能。
摘要：
基于集合的人脸识别（SFR）旨在无约束场景中识别人脸集合，其中同一身份的外观可能因极端方差（如光照、姿态、表情）而发生剧烈变化。我们认为SFR中的两个关键问题——人脸质量和突发性，都与身份无关且与方差相关。质量与突发性评估受到身份纠缠的干扰，而人脸识别则受到方差纠缠的干扰。因此，我们提出在一个轻量级的基于集合的解耦框架中将身份特征与方差特征分离。在解耦之外，方差特征被充分利用来指示集合中的人脸质量和突发性，而不是在训练后被丢弃。为了抑制集合中的人脸突发性，我们提出了一种基于词汇的突发抑制（VBS）方法，该方法使用参考词汇对人脸进行量化。通过对分配分数进行词间和词内归一化操作，适当估计人脸的突发程度。大量图示和实验证明了带有VBS的解耦框架的效果，该框架在SFR基准上取得了新的最先进性能。代码将在 https://github.com/Liubinggunzu/set_burstiness 发布。
英文摘要：
Set-based face recognition (SFR) aims to recognize the face sets in the unconstrained scenario, where the appearance of same identity may change dramatically with extreme variances (e.g., illumination, pose, expression). We argue that the two crucial issues in SFR, the face quality and burstiness, are both identity-irrelevant and variance-relevant. The quality and burstiness assessment are interfered with by the entanglement of identity, and the face recognition is interfered with by the entanglement of variance. Thus we propose to separate the identity features with the variance features in a light-weighted set-based disentanglement framework. Beyond disentanglement, the variance features are fully utilized to indicate face quality and burstiness in a set, rather than being discarded after training. To suppress face burstiness in the sets, we propose a vocabulary-based burst suppression (VBS) method which quantizes faces with a reference vocabulary. With interword and intra-word normalization operations on the assignment scores, the face burtisness degrees are appropriately estimated. The extensive illustrations and experiments demonstrate the effect of the disentanglement framework with VBS, which gets new state-of-the-art on the SFR benchmarks. The code will be released at https://github.com/Liubinggunzu/set_burstiness.
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