Fusion based feature vector for gender classification

In this paper, a feature combining method which can be used in gender classification has been proposed. This method is based on examinating the importance of the pixel regions on face images. In this study, after the analysing commonly used three feature extraction methods (Local binary patterns, discrete cosine transform, histogram of oriented gradients) dimension reduction is achieved via eliminating the redundant face pixels. Then, a new feature vector is obtained by combining the regions considered to be important for each method. When the feature vector´s dimension is reducted, it yields the highest success rate with 95.1% over the 1275 face images.