Abstract
Burstiness, a phenomenon observed in text and image retrieval, refers to that particular elements appear more times in a set than a statistically independent model assumes. We argue that in the context of set-based face recognition (SFR), burstiness exists widely and degrades the performance in two aspects: Firstly, the bursty faces, where faces with particular attributes %exist frequently in a face set, dominate the training instances and dominate the training face sets and lead to poor generalization ability to unconstrained scenarios. Secondly, the bursty faces %dominating the evaluation sets interfere with the similarity comparison in set verification and identification when evaluation. To detect the bursty faces in a set, we propose three strategies based on Quickshift++, feature self-similarity, and generalized max-pooling (GMP). We apply the burst detection results on training and evaluation stages to enhance the sampling ratios or contributions of the infrequent faces. When evaluation, we additionally propose the quality-aware GMP that enables awareness of the face quality and robustness to the low-quality faces for the original GMP. We give illustrations and extensive experiments on the SFR benchmarks to demonstrate that burstiness is widespread and suppressing burstiness considerably improves the recognition performance.
Abstract (translated)
突发性（Burstiness）是文本和图像检索中观察到的现象，指的是某些元素在集合中出现的次数比统计独立模型所假设的要多。我们提出，在基于集合的人脸识别（Set-Based Face Recognition, SFR）中，突发性普遍存在，并从两个方面影响性能：首先，具有特定属性的人脸频繁出现在人脸集中，主导了训练实例和训练集，导致在不受约束场景下的泛化能力较差；其次，在评估集中占主导地位的这些突发性人脸会干扰集合验证和识别过程中的相似度比较。 为了检测集合中突发性的面孔，我们提出了三种策略：基于Quickshift++、特征自相似性和广义最大池化（Generalized Max-Pooling, GMP）的方法。在训练和评估阶段应用突发检测结果以增加稀有面孔的采样比例或贡献值。此外，在评估过程中，我们还提出了一种质量感知GMP方法，使原始GMP能够识别面部质量并提高对低质量面部的鲁棒性。 我们在SFR基准测试上提供了详细的说明和广泛的实验来展示突发性的广泛存在，并表明抑制突发性可显著提升人脸识别性能。
URL
https://arxiv.org/abs/2506.20312
