基于多级纹理频谱特征与PCA的人脸识别算法

Face recognition algorithm based on multi-level texture spectrum features and PCA

针对主成分分析(PCA)算法在人脸识别中识别率低的问题,提出一种图像纹理频谱特征与PCA相结合的人脸识别算法。该算法利用纹理单元算子提取人脸图像纹理频谱特征,然后用PCA对所提取的特征降维,最后利用最近邻(KNN)分类器进行人脸识别。在ORL人脸库和Yale人脸库上对所提出的算法进行了测试,识别率均高于PCA、模块化二维PCA(M2DPCA)等方法,分别为96.5%和95%。实验结果表明了该算法的有效性和准确性。

To improve the recognition rate of Principal Component Analysis （PCA） algorithm in face recognition, a new algorithm combining the image texture spectrum feature with PCA was proposed. Firstly, the texture unit operator was used to extract the texture spectrum feature of the face image. Secondly, PCA approach was used to reduce the dimensions of the texture spectrum feature. Finally, K-Nearest Neighbor （KNN） classification was chosen to recognize the face. ORL and Yale face database were used to test the proposed algorithm, and the recognition accuracies were 96.5% and 95% respectively, which were higher than those of PCA and Modular Two-Dimensional PCA （ M2DPCA）. The experimental results demonstrate the efficiency and accuracy of the proposed algorithm.