An Automatic Face Detection and Gender Classification from Color Images using Support Vector Machine

This paper presents combined face detection and gender classification method of discriminating between faces of men and women. This is done by detecting the human face area in image given and detecting facial features based on the measurements in pixels. The proposed algorithm converts the RGB image into the YCbCr color space to detect the skin regions from the facial image. But in order to detect facial features the color image is converted into gray scale image. This paper presents appearance-based approach with Gabor filter and Support Vector Machine (SVM) classifier. Gabor filter banks are used to extract important facial features, SVM classifier is then used to recognize the facial features. It is proved that SVM can provide superior performance. Different kernel functions have been useful in cases where the data are not linearly separable. These kernel functions transform data to higher dimensional space where they can be separated easily.