Improving the SVM gender classification accuracy using clustering and incremental learning. Issue 3 (21st January 2019)

• Record Type: Journal Article
• Authors: Dagher, Issam; Azar, Fady
• Other Names: Rocha Álvaro guestEditor.; Anwar Sajid guestEditor.
• Is Part Of: Expert systems. Volume 36:Issue 3(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-01-21
• Subjects: classification -- feature selection -- gender recognition -- SVM; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12372
• Languages: English
• ISSNs: 0266-4720

Abstract

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.