Open AccessBank Locker Security System Using Machine Learning with Face and Liveness Detection
Author(s) -
Prof. Mahajan J. S,
Sanika D Gaikwad,
Payal D Kingare,
Vaishnavi A Sunewad,
Bhakti S Gaikwad
Publication year - 2022
Publication title -
international journal of advanced research in science, communication and technology
Language(s) - English
Resource type - Journals
ISSN - 2581-9429
DOI - 10.48175/ijarsct-3152
Subject(s) - liveness , computer science , face (sociological concept) , face detection , artificial intelligence , reliability (semiconductor) , computer vision , facial recognition system , security system , object class detection , pattern recognition (psychology) , computer security , social science , power (physics) , physics , quantum mechanics , sociology , programming language
To increase reliability of face recognition system, the system must be able to distinguish real face from a copy of face such as a photograph. This system introduces a distinguished approach towards Machine learning and Neural Network, in which we propose a fast and memory efficient method of live face detection for embedded face recognition system, based on the analysis of the movement of the eyes. We detect eyes in sequential input images and calculate variation of each eye region to determine whether the input face is a real face or not.