计算机科学与探索2024，Vol.18Issue(7)：1814-1825,12.DOI:10.3778/j.issn.1673-9418.2306082
基于分割和多级掩膜学习的遮挡人脸识别方法
Occluded Face Recognition Based on Segmentation and Multi-stage Mask Learning
摘要
Abstract
Existing face recognition methods cannot effectively eliminate the influence of corrupted features caused by occlusion.As the features flow deeper,the corrupted features get entangled with the effective features used for identity classification,which affects the recognition results.To address the problem,this paper designs an occluded face recognition method based on segmentation and multi-stage mask learning strategy.The model consists of three components:occlusion detection and segmentation,feature extraction,and mask learning unit.The proposed method only needs one end-to-end process to learn feature masks and deep occlusion-robust features without relying on ad-ditional occlusion detectors.The mask learning units take different sizes of occlusion segmentation representations and facial features of different stages as input,generate corresponding feature masks for different stages of feature extraction,and effectively eliminate the influence of corrupted features caused by occlusion at each stage of feature extraction through mask operations.Finally,a feature pyramid is constructed to fuse features of different stages for identity classification.Experimental results show that the proposed method can effectively improve the accuracy of occluded face recognition.The accuracy on the occluded LFW dataset and the real masked datasets MFR2 and Mask_whn reach 98.77%,96.70% and 81.53%,respectively,which has an accuracy improvement of 2.04,0.48 and 4.44 percentage points compared with the existing mainstream methods.关键词
遮挡人脸识别/多级掩膜学习/遮挡检测分割/特征金字塔Key words
occluded face recognition/multi-stage mask learning/occlusion detection and segmentation/feature pyramid分类
信息技术与安全科学引用本文复制引用
张铮,芦天亮,曹金璇..基于分割和多级掩膜学习的遮挡人脸识别方法[J].计算机科学与探索,2024,18(7):1814-1825,12.基金项目
中国人民公安大学网络空间安全执法技术双一流创新研究专项(2023SYL07).This work was supported by the Double First-Class Innovation Research Project for People's Public Security University of China(2023SYL07). （2023SYL07）
