基于SPCA和HOG的单样本人脸识别算法

Face Recognition Using SPCA and HOG with Single Training Image Per Person

基于单样本的人脸识别是一项充满挑战性的任务。文中结合Similar Principal Component Analysis(SPCA)算法与Histograms of Oriented Gradients(HOG)算法,利用SPCA筛选出图像类的相似信息,用HOG算法对相似的信息块进行特征量化,使二者优势互补。最后利用Pearson correlation(PC)进行相似性判别,在数据库Extended Yale B database上进行实验,结果表明,在光照变化的情况下,该算法对人脸正面图像的识别性能比传统算法好。

Face recognition based on single sample is a challenging task.This paper combined the Similar Principal Component Analysis(SPCA)algorithm and Histograms of Oriented Gradients(HOG)algorithm,and used SPCA to screen out the similar information of the image class,and quantified the similar information blocks with HOG algorithm to make the two advantages complementary.Finally,we used Pearson correlation(PC)to identify similarity and conduct experiments on the Extended Yale B database.Experimental results show that the proposed algorithm has better recognition performance than traditional algorithm when the illumination of the face image changes.