基于受试者工作特征pAUC优化的人脸识别系统

基于深度学习的人脸识别技术在大量应用场景中表现出优于传统方法的性能，它们的损失函数大致可分为2类：基于验证的和基于辨识的。验证型损失函数符合开集人脸识别的流程，但实施过程比较困难。因此目前性能较优的人脸识别算法都是基于辨识型损失而设计的，通常由softmax输出单元和交叉熵损失构成，但辨识型损失并没有将训练过程与评估过程统一起来。本文针对开集人脸识别任务提出一种新的验证型损失函数，即最大化受试者工作特征(ROC)曲线下的部分面积(pAUC)；同时还提出一种类中心学习策略提高训练效率，使提出的验证型损失和辨识型损失有较强的可比性。在5个大规模非限定环境下的人脸数据集上的实验结果表明，提出的方法和目前性能最优的人脸识别方法相比，具有很强的竞争性。

Partial Area Under Curve optimization for face recognition system

Deep learning based face recognition has outperformed traditional methods in many application scenarios. There are two main lines of research to design loss functions for face recognition, i.e., verification and identification. The verification loss functions match the pipeline of open-set face recognition, but it is hard to implement. Therefore, most state-of-the-art deep learning methods for face recognition take the identification loss functions with softmax output units and cross-entropy loss. Nevertheless, identification loss function dose not match the training process with evaluation procedure. A verification loss function is proposed for open-set face recognition to maximize partial area under the Receiver-Operating-Characteristic(ROC) curve, partial Area Under Curve(pAUC). A class-center learning method is also proposed to improve training efficiency, which is critical for the proposed loss function to be comparable to the identification loss in performance. Experimental results on five large scale unconstrained face recognition benchmarks show that the proposed method is highly competitive with state-of-the-art face recognition methods.