用于低分辨率人脸识别的深度对手惩罚竞争学习
Deep Rival Penalized Competitive Learning for Low-resolution Face Recognition
摘要：
目前的人脸识别任务通常是在高质量的人脸图像上进行的，但实际上，大多数人脸图像都是在无约束或较差的条件下捕获的，例如，通过视频监控。现有方法的特点是通过学习数据的不确定性来避免过拟合噪声，或者通过向归一化softmax损失的角度或余弦空间添加余量来惩罚目标logit，从而加强类内紧凑性和类间差异。在本文中，我们提出了一种用于低分辨率（LR）图像中的深度人脸识别的深度对手惩罚竞争学习（RPCL）。受RPCL思想的启发，我们的方法进一步对竞争对手logit（定义为输入图像的最大非目标logit）实施监管。与现有的只考虑对目标logit进行惩罚的方法不同，我们的方法不仅加强了对目标标签的学习，而且还执行了一个相反的方向，即，变得去学习化，远离竞争对手的标签。综合实验表明，我们的方法改进了现有的国家的最先进的方法是非常强大的LR人脸识别。
英文摘要：
Current face recognition tasks are usually carried out on high-quality face images, but in reality, most face images are captured under unconstrained or poor conditions, e.g., by video surveillance. Existing methods are featured by learning data uncertainty to avoid overfitting the noise, or by adding margins to the angle or cosine space of the normalized softmax loss to penalize the target logit, which enforces intra-class compactness and inter-class discrepancy. In this paper, we propose a deep Rival Penalized Competitive Learning (RPCL) for deep face recognition in low-resolution (LR) images. Inspired by the idea of the RPCL, our method further enforces regulation on the rival logit, which is defined as the largest non-target logit for an input image. Different from existing methods that only consider penalization on the target logit, our method not only strengthens the learning towards the target label, but also enforces a reverse direction, i.e., becoming de-learning, away from the rival label. Comprehensive experiments demonstrate that our method improves the existing state-of-the-art methods to be very robust for LR face recognition.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
