Comparisons of features for automatic eye and mouth localization

Localization of the eyes and mouth in face images is very important for accurate classification in automatic face recognition systems. The alignment of unknown face images with templates generally improves the performance of the face recognition system, and this process uses locations of the eyes and mouth. In this work, we compare different features (gray-level values, distance transform features, gradients and local binary patterns) for automatic localization of eyes and mouth. To this end, we use the sliding window approach using the linear and nonlinear support vector machine (SVM) classifiers. We created new frontal face data sets to train and test our algorithms. The experimental results show that the SVM classifier using the Gaussian kernel yields better results than the linear kernel. Among the four feature extraction methods, the performance of the local binary pattern features draws the attention for having better detection rates in both the linear and the nonlinear cases with smaller feature size.