基于稀疏表示的图嵌入降维算法在人脸识别中的应用研究

随着社会的发展,公共安全对于人们来说显得愈发重要,如何快速准确的识别生物特征则是重中之重。在应用人脸识别时,通常会因为光照以及人脸的遮挡等客观因素,使得在人脸识别时的准确度降低,进而使得人脸的识别率不高。根据人脸识别过程中的技术需要,使用小波变换和数据降维算法对人脸图像降维变换处理,可以有效的提高人脸识别率。首先通过稀疏表示方法及其构图以及基于图嵌入的降维模型的研究;其中稀疏表示主要对其概念、字典构建以及构图进行研究,然后为了验证改进算法的有效性,在ORL库上进行了一系列的Matlab仿真实验,对提出的方法与其它方法进行对比,从而可以证明提出的基于稀疏表示的图嵌入降维算法在人脸识别中具有比较好的应用效果。

Application of Graph Dimension Reduction Algorithm Based on Sparse Representation in Face Recognition

With the development of society,public safety is becoming more and more important to people. For public safety,how to quickly and accurately identify biological features is the most important. When face recognition is ap-plied,the accuracy of face recognition is reduced because of the objective factors such as illumination and face occlu-sion,which makes the face recognition rate not high. According to the needs of face recognition technology,wavelet transform and data dimensionality reduction algorithm are used to reduce the dimension of face image,in order to ob-tain a higher face recognition rate. Firstly,sparse representation method and its composition and research based on the dimension reduction model of graph embedding;sparse representation which mainly studies its concept,dictionary con-struction and composition,then in order to verify the effectiveness of the improved algorithm,Matlab simulation of a series of experiments in the ORL database,the proposed method with other methods contrast,which can be proved based on the sparse representation of the graph embedding algorithm has good application effects in face recognition.