An Efficient Face Detection and Recognition System Using RVJA and SCNN

Abstract

The basic process for an extensive range of security systems functioning in real-time applications is facial recognition. Considering several factors like lower resolution, occlusion, illumination, noise, along with pose variation, a satisfactory outcome was not achieved by various models developed for face recognition (FR). Therefore, by utilizing reconstruction scheme-centric Viola-Jones algorithm (RVJA) and shallowest sketch-centered convolution neural network (SCNN) methodologies, an effectual face detection and recognition (FDR) system has been proposed here by considering the aforementioned factors. Specifically, first, the algorithm identifies faces in a provided image by determining its global facial model in various positions along with poses; then, it sequentially enhanced the recognition outcome by utilizing SCNN. Initially, by employing the RVJA, face detection (FD) has been performed. The unconstrained face images are handled by the proposed RVJA having efficient properties such as boundedness and invariance, together with the ability to rebuild the actual image. After that, for FR, the SCNN methodology is utilized, thus learning the complicated features of the face-detected images. Next, regarding metrics like area under curve (AUC), recognition accuracy (RA), and average precision (AP), the proposed methodology's experiential outcome is analogized with other prevailing methodologies. The experimental outcome displayed that the facial images are recognized by the proposed model with higher accuracy than that of the other conventional methodologies.