Using Contourlet Transform Based RBFN Classifier for Face Detection and Recognition

Face is a highly non-rigid object; in such case, face detection and recognition has become an essential part of biometric systems in the majority of the applications. Numerous applications like robots, tablets, surveillance systems, and cell phones revolve around an efficient face detection and recognition technique in the background for access. Human–computer interaction systems like expression recognition, cognitive state/emotional state, etc. are used. Recognizing with the increased need for security and anticipation of spoofing attacks, almost all techniques have been proposed in the past to successfully detect and recognize the face through a single or combination of facial features, which is a challenging task given the complex nature of the background and the number of facial features involved. Here, the proposed work involves a multi-resolution technique, namely, the Contourlet transform along with linear discriminant analysis for feature detection given to an RBFN classifier for effective classification. It could be clearly seen that the proposed technique outperforms the other conventional techniques by its recognition rate of nearly 99.2%. The observed results indicate a good classification rate in comparison with conventional techniques.