Score-Level Fusion for Multimodal Biometrics

This thesis describes research into the score-level fusion process in multimodal biometrics. The emphasis of the research is on the fusion of face and voice biometrics in the two recognition modes of verification and open-set identification. The growing interest in the use of multiple modalities in biometrics is due to its potential capabilities for eradicating certain important limitations of unimodal biometrics. One of the factors important to the accuracy of a multimodal biometric system is the choice of the technique deployed for data fusion. To address this issue, investigations are carried out into the relative performance of several statistical data fusion techniques for combining the score information in both unimodal and multimodal biometrics (i.e. speaker and/ or face verification). Another important issue associated with any multimodal technique is that of variations in the biometric data. Such variations are reflected in the corresponding biometric scores, and can thereby adversely influence the overall effectiveness of multimodal biometric recognition. To address this problem, different methods are proposed and investigated. The first approach is based on estimating the relative quality aspects of the test scores and then passing them on into the fusion process either as features or weights. The approach provides the possibility of tackling the data variations based on adjusting the weights for each of the modalities involved according to its relative quality. Another approach considered for tackling the effects of data variations is based on the use of score normalisation mechanisms. Whilst score normalisation has been widely used in voice biometrics, its effectiveness in other biometrics has not been previously investigated. This method is shown to considerably improve the accuracy of multimodal biometrics by appropriately correcting the scores from degraded modalities prior to the fusion process. The investigations in this work are also extended to the combination of score normalisation with relative quality estimation. The experimental results show that, such a combination is more effective than the use of only one of these techniques with the fusion process. The thesis presents a thorough description of the research undertaken, details the experimental results and provides a comprehensive analysis of them

Fusion of cross stream information in speaker verification

This paper addresses the performance of various statistical data fusion techniques for combining the complementary score information in speaker verification. The complementary verification scores are based on the static and delta cepstral features. Both LPCC (Linear prediction-based cepstral coefficients) and MFCC (mel-frequency cepstral coefficients) are considered in the study. The experiments conducted using a GMM-based speaker verification system, provides valuable information on the relative effectiveness of different fusion methods applied at the score level. It is also demonstrated that a higher speaker discrimination capability can be achieved by applying the fusion at the score level rather than at the feature level

Enhancement of multimodal biometric segregation using unconstrained cohort normalisation

Original article can be found at: http://www.sciencedirect.com/science/journal/00313203 Copyright Elsevier Ltd.This paper presents an investigation into the effects, on the accuracy of multimodal biometrics, of introducing unconstrained cohort normalisation (UCN) into the score-level fusion process. Whilst score normalisation has been widely used in voice biometrics, its effectiveness in other biometrics has not been previously investigated. This study aims to explore the potential usefulness of the said score normalisation technique in face biometrics and to investigate its effectiveness for enhancing the accuracy of multimodal biometrics. The experimental investigations involve the two recognition modes of verification and open-set identification, in clean mixed-quality and degraded data conditions. Based on the experimental results, it is demonstrated that the capabilities provided by UCN can significantly improve the accuracy of fused biometrics. The paper presents the motivation for, and the potential advantages of, the proposed approach and details the experimental study. 2007 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.Peer reviewe

Multimodal Authentication using Qualitative Support Vector Machines

This paper proposes an approach to enhancing the accuracy of multimodal biometrics in uncontrolled environments. Variation in operating conditions results in mismatch between the training and test material, and thereby affects the biometric authentication performance regardless of this being unimodal or multimodal. ne paper proposes a technique to reduce the effects of such variations in multimodal fusion. The proposed technique is based on estimating the quality aspect of the test scores and then passing these aspects into the Support Vector Machine either as features or weights. Since the fusion process is based on the learning classifier of Support Vector Machine, the technique is termed Support Vector Machine with Quality Measurement (SVM-QM). The experimental investigation is conducted using face and speech modalities. The results clearly show the benefits gained from learning the quality aspects of the biometric data used for authentication

Score Level Fusion Scheme in Hybrid Multibiometric System

International audienc

Bimodal biometric system hand shape and palmprint recognition based on SIFT sparse representation

International audienceBiometric-based hand modality is considered as one of the most popular biometric technologies especially in forensic applications. In this paper, a bimodal hand identification system was proposed based on Scale Invariant Feature Transform (SIFT) descriptors, extracted from hand shape and palmprint modalities. A local sparse representation method was adopted in order to represent images with high discrimination. Moreover, fusion was performed at feature and decision levels using a cascade fusion in order to generate the final identification rate of our bimodal system. Our experiments were applied on two hand databases: Indian Institute of Technology of Delhi (IITD) hand database and Bosphorus hand database containing, respectively, 230 and 615 subjects. The results show that the proposed method offers high accuracies compared to other popular bimodal hand biometric methods over the two hand databases. The correct identification rate reaches 99.57 % which is competitive compared to systems existing in the literature