A Multimodal Biometric System Using Linear Discriminant Analysis For Improved Performance
This work addresses security needs for users in digital environments, but it is incremental as it applies an existing method to a new multimodal context.
The paper tackles the problem of securing sensitive data by proposing a multimodal biometric system that combines facial and speech recognition using Linear Discriminant Analysis, achieving improved performance in real-time implementation with SignalWAVE.
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.