Development of a Fuzzy Expert System based Liveliness Detection Scheme for Biometric Authentication

TL;DR

This paper introduces a fuzzy expert system for liveliness detection in biometric authentication that is robust under varying illumination and minimal user movement, outperforming traditional vision-based methods.

Contribution

A novel fuzzy expert system for liveliness detection that maintains high accuracy under challenging lighting and movement conditions in real-time biometric authentication.

Findings

False Rejection Rate under bad illumination: 0.28

False Rejection Rate with minimal movement: 0.4

System performs well across different challenging scenarios

Abstract

Liveliness detection acts as a safe guard against spoofing attacks. Most of the researchers used vision based techniques to detect liveliness of the user, but they are highly sensitive to illumination effects. Therefore it is very hard to design a system, which will work robustly under all circumstances. Literature shows that most of the research utilize eye blink or mouth movement to detect the liveliness, while the other group used face texture to distinguish between real and imposter. The classification results of all these approaches decreases drastically in variable light conditions. Hence in this paper we are introducing fuzzy expert system which is sufficient enough to handle most of the cases comes in real time. We have used two testing parameters, (a) under bad illumination and (b) less movement in eyes and mouth in case of real user to evaluate the performance of the system. The system is behaving well in all, while in first case its False Rejection Rate (FRR) is 0.28, and in second case its FRR is 0.4.