Abstract
The paper proposes a likelihood ratio fusion of face, voice and signature multimodal biometrics verification application systems. Figueiredo-Jain (FJ) estimation algorithm of finite Gaussian mixture modal (GMM) is employed. Automated biometric systems for human identification measure a “signature” of the human body, compare the resulting characteristic to a database, and render an application dependent decision. These biometric systems for personal authentication and identification are based upon physiological or behavioral features which are typically distinctive, Multi-biometric systems, which consolidate information from multiple biometric sources, are gaining popularity because they are able to overcome limitations such as non-universality, noisy sensor data, large intra-user variations and susceptibility to spoof attacks that are commonly encountered in mono modal biometric systems. Simulation show that finite mixture modal (GMM) is quite effective in modelling the genuine and impostor score densities, fusion based the resulting density estimates achieves a significant performance on eNTERFACE 2005 multi-modal database based on face, signature and voice modalities.
Highlights
The word biometrics comes from the ancient Greek words: bios living and metros measure, meaning life measurement
[6] Describes a new multimodal biometric system by combining iris, face and voice at match score level using simple sum rule in which the match scores are normalized by min-max normalization and The Experimental evaluations are performed on a public dataset which demonstrating the accuracy of the proposed system
The experiments were performed using still faces, signatures and audio database extracted from video, which is encoded in raw UYVY
Summary
The word biometrics comes from the ancient Greek words: bios living and metros measure, meaning life measurement. It is not susceptible to misplacement or forgetfulness [3] These biometric systems for personal authentication and identification are based upon physiological or behavioral features which are typically distinctive, time varying, such as fingerprints, hand geometry, face, voice, lip movement, gait, and iris patterns. Sheetal Chaudhary et al [6] Describes a new multimodal biometric system by combining iris, face and voice at match score level using simple sum rule in which the match scores are normalized by min-max normalization and The Experimental evaluations are performed on a public dataset which demonstrating the accuracy of the proposed system. A multi-modal biometric verification system based on dynamic facial, signature and vocal modalities is described in this paper Both face images, signature and speech biometrics are chosen due to their complementary characteristics, physiology, and behavior. The enhancement of precision and reliability is the potential result of integrating modalities and/or measurements sensed by multiple sensors [11]
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