基于SVD的人脸对称性的两步人脸识别算法

Using Symmetrical Face Training Samples Based SVD to Perform Representation Based Two-Step Face Recognition

为了缓解人脸图像容易受光照、表情和姿态变化对人脸识别的影响,Yong提出了利用了人脸的对称性产生新的样本来表示人脸特征的方法.这种方法可以反映出人脸样本由于表情、姿态等外在因素引起的变化,一定程度上提高识别效果.但是当样本受外在因素影响产生较大变化时,Yong的方法的识别结果并不理想.而奇异值分解对光照等外在条件引起的灰度变化不敏感,可以缓解人脸对称性在人脸识别中的不足.因此作者在Yong提出的人脸对称性方法的基础上,分别采用SVD和图像镜像的方式构造一幅对称图像则可以缓解其方法中的不足.在ORL、FERET和UMIST三个人脸数据库上进行了重构和识别的实验,并证明了改进算法在人脸重构和识别方面具有明显的优势.

Non-sufficient training samples cannot comprehensively convey the possible changes such as illumination, expression and gesture, so it is hard to improve the accuracy of face recognition. To overcome the problem, Yong proposed a method that exploits the symmetry of the face to generate new samples and perform face recognition. The new training samples really reflect some possible appearance of the face. However, it usually gets bad symmetrical face samples based mirror image with the changes of facial poses, which may affect the accuracy of recognition. The SVD has advantages of stability and shift in-variance, which can ensure the rate of recognition in the case of small changes of face images. To ease the shortage of the above method, the authors improved it by generating ‘symmetrical face＇ training samples based SVD and mirror image, respectively. The experimental results in ORL, PEFET and UMIST databases show that the improved method outperforms the effect of Yong＇s method.