A Secured Biometric Authentication with Hybrid Face Detection and Recognition Model

Abstract

Now-a-days biometrics is used to recognize an individual, and is thought of a more secure way to authorize an individual. Face is the most important factor to recognize an individual. In order to enforce security features, many verification systems use face recognition to identify a person, such as -automobile security, security of home, smartphones owner recognition, attendance system, identification of criminals, etc. Face biometric works in two phases -face detection and face recognition. In this paper we are proposing a hybrid model that works in two phases, face detection is the first phase which includes two stages, in first stage it detects the face from the acquired image and then in the second stage acquired image is subjected to intensity equalization. The second phase is of Face recognition, where finally the individual is recognized as authenticated user or not. We are using Viola-Jones algorithm to detect the face, histogram stretching for intensity equalization and principal component analysis (PCA) algorithm for image recognition. The effectiveness of the suggested facial recognition system is shown through experimental findings using the face dataset constituting of 100 subjects using VTU-BEC-DB multimodal biometrics database with constraints of pose and illumination in the face images, without the intermediate step of intensity equalisation, we had a recognition rate of 93.60%. However, when the intermediate step of intensity equalisation was included, a novelty was added to the hybrid algorithm, and we achieved a recognition rate of 98.20%, which is higher recognition rate than the state-of-the-art methods.