液晶与显示2017，Vol.32Issue(12)：987-992,6.DOI:10.3788/YJYXS20173212.0987
混合样本协同表示算法的人脸识别研究
Face recognition research based on variant samples and collaborative representation
摘要
Abstract
For face recognition,the face image are affected by the variations of expression,lighting,occlusion,pose and especially the number of training samples.However,in practical application,we only have insufficient training samples.The collaborative representation algorithm of combining the original training samples with the axial-symmetry samples is proposed because the original training samples generate the corresponding virtual training samples to increase the number of training samples.Firstly,the original training samples generate the corresponding mirror samples and axial-symmetry samples.Secondly,the reconstruction errors are obtained by using collaborative representation based classification.Finally,the variant reconstruction errors are combined with different weighted number to compare face recognition rates.The experimental results show that the face recognition rates are increased by combining the original training samples with the mirror samples and the axialsymmetry samples.The face recognition rates of combining the original training samples with the axialsy-mmetry samples are 2 ％ ～9 ％ and 1 ％ ～5 ％ better than the original training samples and the original training samples with the mirror samples respectively.It shows that the paper's method is effective in face recognition.关键词
人脸识别/镜像样本/轴对称样本/协同表示/权值融合Key words
face recognition/mirror samples/axial-symmetry samples/collaborative representation/weight fusion分类
信息技术与安全科学引用本文复制引用
杨明中,杨平先,林国军..混合样本协同表示算法的人脸识别研究[J].液晶与显示,2017,32(12):987-992,6.基金项目
四川理工学院科研项目(No.2015RC16)Supported by Scientific Research Project of Sichuan University of Science and Engineering (No.2015RC16) （No.2015RC16）
