基于改进PCA+SVM的人脸识别系统

doi:10.16180/j.cnki.issn1007-7820.2021.12.010

Abstract

Abstract:

The PCA algorithm has low recognition rate for digital image processing, and cannot deal with the non-linear features of the face. In view of this problem, in the basis of original PCA algorithm, a face recognition research method based on KPCA combined with SVM is proposed in this study. By using the internal non-linear kernel function of the KPCA algorithm after the PCA is improved by the kernel, the facial contours of the face are extracted, the non-linear feature data is processed and the data dimension is reduced, which can better reduce the space required for feature data storage and improve the computing power. Then, combined with the SVM classifier for classification and recognition, the system recognition rate is improved. Experiments show that the recognition rate of the proposed algorithm in the ORL face database is 95.16%, and the recognition rate in the Yale face database is 95.10%. The system established on MATLAB can correctly recognize human faces, which proves the feasibility of the system proposed in the study, and has certain reference value for actual research.

Key words: face recognition, principal component analysis, KPCA, Gamma correction method, SVM, dimensionality reduction, classification recognition, GUI

CLC Number:

TP391.4

PENG Rongjie,PENG Yaxiong,LU Anjiang. Face Recognition System Based on Improved PCA+SVM[J].Electronic Science and Technology, 2021, 34(12): 56-61.

Figures/Tables 7

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Table 1

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References 19

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算法	训练样本数	测试样本数	识别率/%	平均运行时间/s
PCA	5	5	77.00	4.45
PCA+SVM	5	5	84.00	4.38
KPCA+SVM	5	5	95.16	3.95

算法	训练样本数	测试样本数	识别率/%	平均运行时间/s
PCA	5	5	74.00	5.27
PCA+SVM	5	5	83.10	6.22
KPCA+SVM	5	5	95.10	5.12

Face Recognition System Based on Improved PCA+SVM

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