Gender Recognition Using a Min-Max Modular Support Vector Machine

• Published in: Advances in Natural Computation pp. 438 - 441
• Main Authors: Lian, Hui-Cheng, Lu, Bao-Liang, Takikawa, Erina, Hosoi, Satoshi
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2005; Springer
• Series: Lecture Notes in Computer Science
• Subjects: Applied sciences; Artificial intelligence; Computer science; control theory; systems; Exact sciences and technology; Pattern recognition. Digital image processing. Computational geometry; Gabor filter; Image analysis; Statistical analysis; Probabilistic approach; Face recognition; Sexing; Facies; Minimax method; High precision; Vector support machine; Problem solving; Pattern extraction
• ISBN: 9783540283256; 3540283250; 3540283234; 9783540283232
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/11539117_64

Considering the fast respond and high generalization accuracy of the min-max modular support vector machine (M3-SVM), we apply M3-SVM to solving the gender recognition problem and propose a novel task decomposition method in this paper. Firstly, we extract features from the face images by using a facial point detection and Gabor wavelet transform method. Then we divide the training data set into several subsets with the ‘part-versus-part’ task decomposition method. The most important advantage of the proposed task decomposition method over existing random method is that the explicit prior knowledge about ages contained in the face images is used in task decomposition. We perform simulations on a real-world gender data set and compare the performance of the traditional SVMs and that of M3-SVM with the proposed task decomposition method. The experimental results indicate that M3-SVM with our new method have better performance than traditional SVMs and M3-SVM with random task decomposition method.