Abstract
In this paper, a novel face recognition system for face recognition and identification based on a combination of Principal Component Analysis and Kernel Canonical Correlation Analysis (P-KCCA) using Support Vector Machine (SVM) is proposed. First, the P-KCCA method is utilized to detect and extract the important features from the input images. This method makes it possible to match the 2D face image with enrolled 3D face data. The resulting features are then classified using the SVM method. The proposed methods were tested on TEXAS database with 200 subjects. The experimental results in the TEXAS face database produce interesting results from the point of view of recognition success, rate, and robustness of the face recognition algorithm. We compare the performance of our proposed face recognition method to other commonly-used methods. The experimental results show that the combination of P-KCCA method using SVM achieves a higher performance compared to the alone PCA, CCA and KCCA algorithms.
Highlights
Face detection is a very useful subject and it plays an important role in the real applications
A novel face recognition system for face recognition and identification based on a combination of Principal Component Analysis and Kernel Canonical Correlation Analysis (P-KCCA) using Support Vector Machine (SVM) is proposed
The experimental results show that the combination of P-KCCA method using SVM achieves a higher performance compared to the alone Principal Component Analysis (PCA), CCA and KCCA algorithms
Summary
Face detection is a very useful subject and it plays an important role in the real applications. The recent interest in face recognition can be attributed to the increase of commercial interest and the development of feasible technologies to support the development of face recognition [1]. Many methods and approaches have been proposed in the literature. Two of these approaches, namely the combination of principal component analysis with kernel canonical correlation analysis (P-KCCA) and support vector machines (SVM) are the subject of this paper. The problem of human face recognition is complex and highly challenging because it has to deal with a variety of parameters including illumination, pose orientation and face background [2] and [3]
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