Abstract
In recent years, the emergence of deep convolutional neural networks has positioned face recognition as a prominent research focus in computer vision. Traditional loss functions, such as margin-based, hard-sample mining-based, and hybrid approaches, have achieved notable performance improvements, with some leveraging curriculum learning to optimize training. However, these methods often fall short in effectively quantifying the difficulty of hard samples. To address this, we propose Adaptive Sine (AdaSin) loss function, which introduces the sine of the angle between a sample's embedding feature and its ground-truth class center as a novel difficulty metric. This metric enables precise and effective penalization of hard samples. By incorporating curriculum learning, the model dynamically adjusts classification boundaries across different training stages. Unlike previous adaptive-margin loss functions, AdaSin introduce a dual adaptive penalty, applied to both the positive and negative cosine similarities of hard samples. This design imposes stronger constraints, enhancing intra-class compactness and inter-class separability. The combination of the dual adaptive penalty and curriculum learning is guided by a well-designed difficulty metric. It enables the model to focus more effectively on hard samples in later training stages, and lead to the extraction of highly discriminative face features. Extensive experiments across eight benchmarks demonstrate that AdaSin achieves superior accuracy compared to other state-of-the-art methods.
Abstract (translated)
近年来，深度卷积神经网络的出现将人脸识别确立为计算机视觉领域的一个重要研究焦点。传统的损失函数，如基于边距、基于难例挖掘和混合方法等，已经实现了显著的性能改进，其中一些通过课程学习来优化训练过程。然而，这些方法往往无法有效地量化难样本的难度。为此，我们提出了自适应正弦（AdaSin）损失函数，该函数引入了样本嵌入特征与其真实类别中心之间的夹角正弦值作为新的难度度量标准。这一指标能够精准且有效地区分并惩罚难样本。通过结合课程学习，模型能够在不同的训练阶段动态调整分类边界。 与之前的自适应边距损失函数不同，AdaSin引入了一种双重自适应惩罚机制，分别应用于硬样本的正余弦相似性和负余弦相似性上。这种设计施加了更强的约束条件，增强了类内紧致度和类间分离度。通过精心设计的难度度量标准来指导双重自适应惩罚与课程学习相结合的方式，使得模型能够更有效地关注后期训练阶段中的难样本，并最终提取出高度判别性的面部特征。 在八个基准数据集上的广泛实验表明，AdaSin相比其他最先进的方法取得了更为优越的准确率。
URL
https://arxiv.org/abs/2503.03528
