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Research and Implementation of PCA Face Recognition Algorithm Based on Matlab

机译:基于Matlab的PCA人脸识别算法的研究与实现

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This paper researches the theory of PCA (Principle Component Analysis) algorithm and the feature extraction elements in the process of face recognition, summarizes application procedures of PCA algorithm in the process face recognition, and realizes the application of PCA algorithm in the process face recognition in the matlab software. The research content and realization results show that: PCA algorithm is a kind of algorithm which is very suitable for programming and realization of matlab software; the key factor to realize PCA algorithm is the selection of the number of feature vectors, which affects the recognition rate and training time of the space sample subset. The higher recognition rate indicates better results in the algorithm implementation; the shorter training time of the space sample subset indicates more excellent algorithm implementation. In the process of selection of the number of feature vectors, on one hand, there is a need to protect the recognition rate; on the other hand, there is a need to control training time of the space sample subset, in which the recognition rate is a rigid target. The shortest training time of the subset of samples is selected on the premise of meeting the recognition rate.Key words: PCA algorithm / face recognition / training time / recognition rate / matlab realization
机译:本文研究了PCA(主成分分析)算法的理论和人脸识别过程中的特征提取要素,总结了PCA算法在过程人脸识别中的应用程序,实现了PCA算法在人脸识别中的应用。 matlab软件。研究内容和实现结果表明:PCA算法是一种非常适合于matlab软件的编程和实现的算法;实现PCA算法的关键因素是特征向量数量的选择,这会影响空间样本子集的识别率和训练时间。较高的识别率表明算法实现的效果更好;空间样本子集的训练时间越短,表明算法实现越出色。在选择特征向量的数量的过程中,一方面,需要保护识别率。另一方面,需要控制其中识别率是刚性目标的空间样本子集的训练时间。在满足识别率的前提下,选择样本子集的最短训练时间。关键词:PCA算法/人脸识别/训练时间/识别率/ matlab实现

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