A Multimodal Biometric System Using Linear Discriminant Analysis For Improved Performance

TL;DR

This paper presents a multimodal biometric system combining facial and speech recognition using Linear Discriminant Analysis to enhance security and robustness, implemented in real-time with SignalWAVE.

Contribution

It introduces a multimodal biometric system utilizing LDA for both facial and speech recognition, improving security and handling enrollment failures.

Findings

Enhanced security through multimodal approach

Real-time implementation with SignalWAVE

Improved recognition accuracy

Abstract

Essentially a biometric system is a pattern recognition system which recognizes a user by determining the authenticity of a specific anatomical or behavioral characteristic possessed by the user. With the ever increasing integration of computers and Internet into daily life style, it has become necessary to protect sensitive and personal data. This paper proposes a multimodal biometric system which incorporates more than one biometric trait to attain higher security and to handle failure to enroll situations for some users. This paper is aimed at investigating a multimodal biometric identity system using Linear Discriminant Analysis as backbone to both facial and speech recognition and implementing such system in real-time using SignalWAVE.