Please use this identifier to cite or link to this item: https://scholarhub.balamand.edu.lb/handle/uob/2115
Title: Improving the SVM gender classification accuracy using clustering and incremental learning
Authors: Dagher, Issam 
Azar, Fady
Affiliations: Department of Computer Engineering 
Keywords: Feature selection
Gender recognition
SVM
Subjects: Classification
Issue Date: 2019
Part of: Journal of expert systems
Volume: 36
Issue: 3
Start page: 1
End page: 17
Abstract: 
Gender recognition has been playing a very important role in various applications such as human–computer interaction, surveillance, and security. Nonlinear support vector machines (SVMs) were investigated for the identification of gender using the Face Recognition Technology (FERET) image face database. It was shown that SVM classifiers outperform the traditional pattern classifiers (linear, quadratic, Fisher linear discriminant, and nearest neighbour). In this context, this paper aims to improve the SVM classification accuracy in the gender classification system and propose new models for a better performance. We have evaluated different SVM learning algorithms; the SVM‐radial basis function with a 5% outlier fraction outperformed other SVM classifiers. We have examined the effectiveness of different feature selection methods. AdaBoost performs better than the other feature selection methods in selecting the most discriminating features. We have proposed two classification methods that focus on training subsets of images among the training images. Method 1 combines the outcome of different classifiers based on different image subsets, whereas method 2 is based on clustering the training data and building a classifier for each cluster. Experimental results showed that both methods have increased the classification accuracy.
URI: https://scholarhub.balamand.edu.lb/handle/uob/2115
DOI: 10.1111/exsy.12372
Ezproxy URL: Link to full text
Type: Journal Article
Appears in Collections:Department of Computer Engineering

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