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A Hybrid Face Recognition Method based on Face Feature Descriptors and Support Vector Machine Classifier
Rafika Harrabi Harrabi
Pages - 1 - 14     |    Revised - 01-03-2022     |    Published - 30-04-2022
Volume - 16   Issue - 1    |    Publication Date - April 2022  Table of Contents
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KEYWORDS
Face Recognition, Feature Extraction, HOG, SVM, DCT, Gabor, Classification.
ABSTRACT
Face recognition is a technique used to identify/verify human identity based on their facial features. A technique allows, based on facial features to authenticate / identify a person. However, for human identification or identity authentication based on face recognition technology, the appropriate determination of the face features plays a crucial role, since the identification of the Human is given directly by the classification of these characteristics.

In this paper, we propose a new face recognition method based on face feature descriptors and Support Vector Machine (SVM) algorithm. The face feature descriptors are used to extract and select the statistical features, whereas, the SVM algorithm is employed to classify the different features and to obtain optimal Human face recognition.

The feature extraction step is the major phase of the recognition cycle. It is employed to extract the features for any human face located in the first step. The accomplishment of this step controls the success of subsequent steps. For that, the main objective of this work is to determine of the best method of feature extraction.

To do the indexation of person’s face, the Histogram of Oriented Gradient features (HOG), Gabor features and Discrete Cosine Transform features (DCT) are employed to extract the feature vectors for any human face.

In addition, the face recognition method, proposed in this paper, is conceptually different and explores a new strategy. In fact, instead of considering an existing face recognition procedure, the proposed technique rather explores the benefit of combining several approaches.This method is a hybrid face recognition technique, which integrates both the results of the HOG, and the SVM technique, in which the HOG method is used as the initial seed for the classification procedure.

Experimental results from the proposed method are validated and the face recognition rate for the ''ORL'' and cropped ''Yale B'' datasets is evaluated, and then a comparative study versus existing techniques is presented. The highest face recognition rate of the used dataset is obtained by the proposed method. In addition, the use of the proposed HOG_SVM method to build face recognition systems can achieve excellent results when the dataset size is large, and therefore it can be used in different security and authentication systems.
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Dr. Rafika Harrabi Harrabi
Industrial Innovation and Robotics Center, University of Tabuk, Tabuk, Kingdom Saudi Arabia
University of Tunis, Department of Electrical Engineering, CEREP, ENSIT 5 Av, Taha Hussein, 1008, Tunis, Tunsia - Saudi Arabia
rharrabi@ut.edu.sa


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