Face recognition based on statistical features and SVM classifier

• Published in: Multimedia tools and applications Vol. 81; no. 6; pp. 8767 - 8784
• Main Authors: Chaabane, Slim Ben, Hijji, Mohammad, Harrabi, Rafika, Seddik, Hassene
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.03.2022; Springer Nature B.V
• Subjects: Algorithms; Comparative studies; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Face recognition; Feature extraction; Multimedia Information Systems; Special Purpose and Application-Based Systems; Statistical analysis; Support vector machines; Face recognition; Classification; Feature extraction; True success rate; Support vector machine; Statistical features; Principal component analysis
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-021-11816-w

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%.