A Model for Real-Time Biometric Authentication Using Facial and Hand Gesture Recognition

Abstract

Facial recognition is the process of identifying and/or verifying distinct facial characteristics of an individual in an image or a video stream, pre-recorded or in real time. Hand gesture recognition refers to the process of identifying the configuration of an individual’s hand posture in an image and can be used to verify concurrency of it with respect to a pre-set randomized prompt example to use it in a way similar to CAPTCHA. This paper aims to unify these two similar, yet varying image processing technologies in order to be able to provide a multi-factor authentication system for web applications which cannot be tricked using pre-recorded footage. The face would be identified in the image using convolutional neural networks and then be masked from the image for the hand to be isolated by identifying them using the skin tone. Once the hand has been isolated, SIFT points identified would determine the gesture being performed by the subject, followed by matching the face with the requested credentials if approved. The facial recognition would act like a password and the hand gesture recognition as a means to verify the authenticity of the facial credentials provided.