An Efficient Approach towards Face Recognition using Deep Reinforcement Learning, Viola Jones and K-nearest neighbor

Abstract

Authentication of a user's identity is becoming a tough task for a system in today's era in which digital authentication becoming mandatory to satisfy the security of a system. Recognition failure of user's identity is one of the big concerns. This paper introduces an efficient mechanism to carry out the recognition of facial features in order to satisfy the authentication of a system. Earlier researches in this field have common constraints such as false acceptance and false rejection rate. The proposed method implements over video data on which deep reinforcement learning and K-nearest neighbors (KNN) have been applied to perform detection and recognize facial data accurately. The challenging task of this work is to correctly recognize the facial data under various disturbance and unprecedented noisy circumstances including bad illumination, blurring, inappropriate poses, angle, etc. The main objective of the model is to achieve a high recognition rate of facial data under different unwanted noise and attacks. Reinforcement learning is used to count the number of people in the proposed system. This concept of the KNN algorithm is used for classification based on Euclidean distance to achieve better recognition results. The average rate of accuracy for recognition is found to be 96.40%. The proposed model can be applied to an investigation into digital forensics.