Face recognition based on statistical features and SVM classifier

被引：21

作者：

Ben Chaabane, Slim ^{[1
,2
]}

Hijji, Mohammad ^{[3
]}

Harrabi, Rafika ^{[1
,2
]}

Seddik, Hassene ^{[2
]}

机构：

[1] Univ Tabuk, Comp Engn Dept, Tabuk 47512, Saudi Arabia

[2] Univ Tunis, Elect Engn Dept, CEREP, ENSIT 5 Av, Tunis 1008, Tunisia

[3] Univ Tabuk, Comp Sci Dept, Tabuk 47512, Saudi Arabia

来源：

MULTIMEDIA TOOLS AND APPLICATIONS | 2022年 / 81卷 / 06期

关键词：

Face recognition; Feature extraction; Statistical features; Principal component analysis; True success rate; Support vector machine; Classification;

D O I：

10.1007/s11042-021-11816-w

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, a face recognition method based on statistical features and Support Vector Machine (SVM) algorithm is proposed. The statistical analysis is used to extract and select the statistical features, whereas, the SVM algorithm is employed to merge and classify the different features in order to increase the quality of the information and to obtain an optimal Human face recognition. Human face recognition results from the proposed method are validated and the True Success Rate (TSR) for the test data available is evaluated, and then a comparative study versus existing techniques is presented. The experimental results with 400 test images of 40 persons demonstrate the superiority of introducing the statistical features in SVM algorithm for human face recognition. In addition, the recognition speed of our method is faster than the classical SVM algorithm and other existing methods. Experimental results show that the algorithm identifies the face images with accuracy of 99.37%.

引用

页码：8767 / 8784

页数：18

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