中山大学学报（自然科学版）2016，Vol.55Issue(5)：48-51,56,5.DOI:10.13471/j.cnki.acta.snus.2016.05.009
核主成分分析网络的人脸识别方法
Kernel principal component analysis network method for face recognition
摘要
Abstract
Principal component analysis network (PCANet)is a popular deep learning classification method,which has caused wide attention in the area of computer vision due to its practical applications in face recognition,hand-written digit recognition,texture classification,and object recognitions.On the basis of PCANet.The kernel principal component analysis network (KPCANet)method is proposed for face recognition.The model is constructed by four processing components,including principal component analysis (PCA),kernel principal component analysis (KPCA),binary hashing,and block-wise histo-grams.The performance of the proposed method is evaluated using two public face datasets,i.e.,Ex-tended Yale B database and AR face database.The results show that KPCANet outperforms PCANet method.Especially when the face images have large variations about illuminations and expressions,KP-CANet gives better recognition results.关键词
核主成分分析网络/深度学习/人脸识别/核变换Key words
kernel principal component analysis network/deep learning/face recognition/kernel trans-formation分类
计算机与自动化引用本文复制引用
胡伟鹏,胡海峰,顾建权,李昊曦..核主成分分析网络的人脸识别方法[J].中山大学学报（自然科学版）,2016,55(5):48-51,56,5.基金项目
国家自然科学基金资助项目（60802069，61273270）；广东省自然科学基金资助项目 （）
