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

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 fa...