Reduced complexity and optimized face recognition approach based on facial symmetry

Recognition of face images is still a challenging and open research problem. A number of recent algorithms have shown that there is a vast scope in improving recognition accuracy by utilizing facial symmetry for face recognition task. The lower computational complexity and faster processing times make this method well suited for real-time applications. In this paper, we have used only one half of the face image for recognition task against various facial challenges. Keeping in view all the previous related studies that are limited in their scope, an unbiased comparison is presented between full face images and half face images by applying four subspace-based algorithms with four different distance metrics. Experiments are conducted on the two most challenging face databases. The FERET is a benchmark database, which closely simulates real-life scenarios, and LFW which is developed for the problem of unconstrained face recognition.