针对高质量难样本的面部识别，提出自适应损失函数提升准确率。
LH2Face: Loss function for Hard High-quality Face
- 基于vMF分布设计相似性度量，引入不确定性感知的可调边界。
- 在IJB-B数据集上达49.39%准确率，比第二名高2.37个百分点。
- 适合高难度高质量人脸验证场景，尤其适用于精准身份认证系统。
当前主流人脸识别系统多采用余弦相似度结合Softmax分类，但在处理难样本时表现不佳。现有方法虽引入角度或余弦边界，但未考虑人脸质量与识别难度，导致训练策略过于统一。为此，本文提出新型损失函数LH2Face：首先基于von Mises-Fisher（vMF）分布定义概率密度函数对数作为相似性度量；其次设计不确定性感知的可调边界分类方法；再通过代理样本约束优化特征空间分布；最后构建双向重建-识别渲染器协同优化。实验表明，该方法在硬样本高质量人脸数据集上表现优异，在IJB-B数据集上达到49.39%准确率，超越第二名2.37个百分点。
原文摘要 · Abstract (English)
In current practical face authentication systems, most face recognition (FR) algorithms are based on cosine similarity with softmax classification. Despite its reliable classification performance, this method struggles with hard samples. A popular strategy to improve FR performance is incorporating angular or cosine margins. However, it does not take face quality or recognition hardness into account, simply increasing the margin value and thus causing an overly uniform training strategy. To address this problem, a novel loss function is proposed, named Loss function for Hard High-quality Face (LH2Face). Firstly, a similarity measure based on the von Mises-Fisher (vMF) distribution is stated, specifically focusing on the logarithm of the Probability Density Function (PDF), which represents the distance between a probability distribution and a vector. Then, an adaptive margin-based multi-classification method using softmax, called the Uncertainty-Aware Margin Function, is implemented in the article. Furthermore, proxy-based loss functions are used to apply extra constraints between the proxy and sample to optimize their representation space distribution. Finally, a renderer is constructed that optimizes FR through face reconstruction and vice versa. Our LH2Face is superior to similiar schemes on hard high-quality face datasets, achieving 49.39% accuracy on the IJB-B dataset, which surpasses the second-place method by 2.37%.
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