Face Recognition Based on Coarse Sub-bands of Contourlet Transformation and Principal Component Analysis

Abstract

In this paper, a face recognition system, by using Contourlet transform (CT) as a two dimensional discrete transform and principal component analysis (PCA) as a sub-space method to form the feature vectors, is implemented. Any input image is decomposed by CT up to three levels and the CT coefficients are obtained at three scales and 15 orientations. The obtained CT coefficients are used by PCA to form the feature vectors. At the end, the Euclidean distance is used for classification. Our experimental results on ORL data base show the appropriate performance in comparison with other approaches even though for each subject only one image is used for training and other 9 images are used for testing. The average accuracy of our proposed algorithm for face recognition is 96.07%. 人脸识别基于Contourlet变换和主成分分析的粗子带伊尔哈姆哈希米鲥鱼， 抽象 在本文中，面部识别系统，通过使用轮廓波变换（CT），其为二维离散变换和主成分分析（PCA），为子空间法，以形成特征向量，实现的。任何输入图象是通过CT分解最多三个水平和三个尺度和15取向获得的CT系数。将所得的CT系数由PCA用于形成特征向量。在结束时，欧几里德距离被用于分类。我们在ORL数据的基础的实验结果表明，与其他的方法，即使对于每个受试者仅一个图像用于培训和其他9图像用于测试比较的适当的性能。我们提出的算法用于人脸识别的平均精度为96.07ï¼