Fuzzy Support Vector Machines for Age and Gender Classification

Support vector machine (SVM) has been proven as a powerful tool for solving age and gender classification problems. However, SVM is sensitive to noise and outliers. In this paper we propose a new fuzzy SVM based on an assumption that training data points should not be treated equally to avoid the problem of sensitivity to noise and outliers. This can be achieved by assigning a fuzzy membership as a weight to each training data point. A method to calculate fuzzy memberships is also presented. Experiments performed on the aGender corpus for INTERSPEECH 2010 Paralinguistic Challenge show that the proposed fuzzy SVM can improve age and gender classification accuracy.