Self-paced Robust Deep Face Recognition with Label Noise

摘要：

　　Deep face recognition has achieved rapid development but still suffers from occlusions,illumination and pose variations,especially for face identification.The success of deep learning models in face recognition lies in large-scale high quality face data with accurate labels.However,in real-world applications,the collected data may be mixed with severe label noise,which significantly degrades the generalization ability of deep models.To alleviate the impact of label noise on face recognition,inspired by curriculum learning,we propose a self-paced deep learning model(SPDL)by introducing a negative l1-norm regularizer for face recognition with label noise.During training,SPDL automatically evaluates the cleanness of samples in each batch and chooses cleaner samples for training while abandons the noisy samples.To demonstrate the effectiveness of SPDL,we use deep convolution neural network architectures for the task of robust face recognition.Experimental results show that our SPDL achieves superior performance on LFW,MegaFace and YTF when there are different levels of label noise.

关键词： Face recognition Label noise Self-pace learning

作者: Pengfei Zhu Wenya Ma Qinghua Hu

作者单位: College of Intelligence and Computing,Tianjin University,Tianjin,China

会议类型: 国际会议

会议名称: The 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (第23届亚太知识发现和数据挖掘国际会议(PAKDD2019)

会议地点: 澳门

会议语种:英文

页码: 425-435

在线出版日期: 2019-04-14（万方平台首次上网日期，不代表论文的发表时间）