Automatic face recognition from frontal images

Face recognition can be described as identification of people from their face images. In this study, an automatic face recognition system has been designed by using frontal images photographed in our lab. The automatic face recognition procedure consists of an alignment process which includes face detection, eye detection, mapping of the center coordinates of the eyes to a standard face template. This is followed by classification of aligned faces. In literature, face alignment process is usually done with manually and high recognition rates can be achieved due to very well aligned faces. However, in real-time face recognition applications, it's not possible to align face images manually. Therefore, successful classification rates reported in the literature are mostly misleading. In this study, we aligned faces in a fully automatic manner and we obtained more reliable and realistic face recognition rates. Face images are represented with gray level, LBP, LTP, and two dimensional Gabor filter features and performances are tested with Eigenfaces, Fisherfaces, and DCV methods. Experimental results showed that the automatic recognition rates can reach close to 90% correct recognition rates.