CAJ | 학술논문

人脸识别的识别率受众多因素影响，目前已有很多成形的高识别率算法，然而，随着数据库中人脸图像的增加，识别率下降很快。鉴于该特点，采用频域下的稀疏表示分类算法能有效解决上述问题，先使用快速傅里叶变换(FFT)将人脸数据从时域变换到频域，再通过 l 1范数最优化稀疏表示算法，把所有训练样本作为基向量，稀疏表示出测试样本，最后使用最近邻子空间算法分类。在扩展的 Yale B 人脸库中实验结果表明，该算法具有有效性。
인검식별적식별솔수음다인소영향，목전이유흔다성형적고식별솔산법，연이，수착수거고중인검도상적증가，식별솔하강흔쾌。감우해특점，채용빈역하적희소표시분류산법능유효해결상술문제，선사용쾌속부리협변환(FFT)장인검수거종시역변환도빈역，재통과 l 1범수최우화희소표시산법，파소유훈련양본작위기향량，희소표시출측시양본，최후사용최근린자공간산법분류。재확전적 Yale B 인검고중실험결과표명，해산법구유유효성。
The recognition rate of face recognition is influenced by many factors, in which there are lots of effective algo-rithms, however, with the increase of face in the database, and the recognition rate will be decreased rapidly. In this situation, the sparse representation classification under the frequency domain can solve the above problems effectively. Firstly, the face image will be transformed from time domain to frequency domain using FFT algorithm, and then sparse representation about the test sample will be obtained by l1 norm optimization approach, in which all the training samples as the base vectors, in addition using the nearest neighbor subspace classification. Finally the experimental results show that the algorithm is effective in the extensional Yale B face database.