基于差空间的双向2DPCA和SVM人脸识别算法

摘要

为降低SVM人脸识别算法对样本进行训练和识别的时间,提出了一种改进的基于差空间的双向2DPCA (Bidirectional two dimensions PCA)和SVM相结合的人脸识别算法.该方法充分考虑了表情和光照对人脸图像的影响,不但利用小波变换对人脸图像进行预处理,而且成功地把类内平均引入到双向2DPCA的计算中,并结合了SVM在分类识别方面的优势,有效节省了算法所需的时间.在Yale人脸库上的实验表明,它不但可以提高识别率,而且所用时间明显减少.%A novel face recognition algorithm was proposed to save the time of sample training and face recognition based on SVM. The new method is to recognize face based on residual space and SVM with Bidirectional two dimensions PCA To avoid the influence of expression and light on face recognition, wavelet transform was used to process face images at first, then the within-class average was applied to the calculate two dimensions PCA. Furthermore SVM is in classification in order to effectively decrease the time of arithmetic. Experiments on Yale face data show that the new method can not only improve recognition rate, but also save the recognition time.