Fusion of DRL and CNN for Effective Face Recognition

Digital Authentication combined with IOT networks is one of the most innovative practices in innovations. IoT devices such as surveillance cameras have acquired real-time video data in the natural environment. Facial Biometrics for System authentication is considered to be a sophisticated technology used in various devices. The correct processing of digital information in a natural environment is one of the challenging tasks. Noise effect inducts random variations on the subject in the natural environment that may hinder the recognition efficiency of the model. The proposed system recognizes facial biometric features under the natural environment that may contain non-symmetrical randomness of variations. The system's objective is to mitigate the false recognition rates under adverse environmental conditions. It combines digital information with a person’s real-time environment. The detection of the facial region of interest form frames in a real-time dataset has been taken through the Viola-Jones algorithm. Feature extrication using video frames has been developed through Deep Reinforcement Learning (DRL) algorithm, which aims to generate binary trees containing feature vectors. Further, the system uses a convolutional neural network (CNN) model to establish the correlation of feature vectors belonging to the facial identity. The model's objective is to retain high recognition of facial biometric trait feature units under various randomness in a natural environment. The proposed system is also tested under various attacks to test the robustness of the proposed model. The model can secure an average accuracy of 98.85%. Digital forensics, crime investigation, online attendance, etc., are the few applications of Facial data authentication from dynamic input.