基于样本扩充的核稀疏表示的人脸识别方法

摘要

为进一步提高基于稀疏表示的人脸识别方法识别率，提出一种基于样本扩充的核稀疏表示方法(KSRMSE)。通过在原始样本中添加少量的噪声，扩大原始样本集的规模，使用核诱导函数从训练样本集中挑选N个最近邻样本，利用这N个最近邻样本的线性组合表示测试样本，根据表示的结果对测试样本进行分类，通过修改N值获得更高的分类精度。实验结果表明，相比同类识别算法，该方法具有更好的识别效果。%To improve the recognition rate of face recognition method based on sparse representation,a kernal sparse representa-tion method based on samples expansion method (KSRMSE)was proposed.The training samples were extended to form a new training set by adding some noise to them and a kernel-induced function was used to determine N nearest neighbors of the testing sample from the total training samples.The testing sample was represented using the linear combination of determined N nearest neighbors and the classification was implemented according to the representation results.Through the different values of N set, the classification was more accurate.Experimental results show that KSRMSE can get better classification results than the same type of algorithms.