Automatic segmentation of training set for facial feature detection

In conventional image-based feature detection a time consuming pre-processing step is required to manually segment the training features from the unsegmented face images. We present a novel method of using automatically segmented facial image data for facial feature detection. A quality measure is defined to identify those image data from a large training set that are better to describe the feature. The best quality subset is then extracted and used to train the feature detector. The detection performance obtained by the automatically segmented data set after refinement is almost as high as that obtained by the feature detector trained by a manually segmented set.