FACE RECOGNITION BASED ON CONCATENATION OF SPATIAL DOMAIN FEATURES

Face recognition is one of most popular application in the electronic security system to identify a person as it is contactless and non-invasive. However, it is a big challenging task to achieve the better accuracy due to variations in intensity, illumination, orientation, different pose and facial expressions. To handle these constraints effectively, we propose a hybrid domain based face recognition using Asymmetric Region Local Binary Pattern (ARLBP) and Histogram oriented gradient (HOG) techniques. The preprocessing has been carried out on all the images to extract the face region by removing the background and resizing to 100x100. The face features are extracted using ARLBP and HoG techniques for different face databases. The obtained features are fused by concatenation and compared with trained set of features using Euclidean distance classifier. The Performance was evaluated by measuring False Acceptance Rate (FAR), False Rejection Rate (FRR), Total Success Rate (TSR) and Equal Error Rate (EER) for L space k, JAFFE and YALE Databases. It is found that L space k database has the least equal error rate among the databases studied and also we have achieved a better recognition rate for YALE database compared to the other state-of- the-art methods.