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Automatic Image Collection of Objects with Similar Function by Learning Human Grasping Forms Read chapter Read first chapter

2015 | OriginalPaper | Chapter

A SIFT-Based Feature Level Fusion of Iris and Ear Biometrics

Authors : Lamis Ghoualmi, Salim Chikhi, Amer Draa

Published in: Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction

Publisher: Springer International Publishing

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Abstract

To overcome the drawbacks encountoured in unimodal biometric systems for person authentification, multimodal biometrics methods are needed. This paper presents an efficient feature level fusion of iris and ear images using SIFT descriptors which extract the iris and ear features separetely. Then, these features are incorporated in a single feature vector called fused template. The generated template is enrolled in the database, then the matching of SIFT features of iris and ear input images and the enrolled template of the claiming user is computed using Euclidean distance. The proposed method has been applied on a synthetic multimodal biometrics database. The latter is produced from Casia and USTB 2 databases wich represent iris and ear image sets respectively. As the performance evaluation of the proposed method we compute the false rejection rate (FRR), the false acceptance rate (FAR) and accuracy measures. From the obtained results, we can say that the fusion at feature level outperforms iris and ear authentification systems taken separately.

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previous chapter A Real Time Gesture Recognition System for Human Computer Interaction
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Metadata
Title
A SIFT-Based Feature Level Fusion of Iris and Ear Biometrics
Authors
Lamis Ghoualmi
Salim Chikhi
Amer Draa
Copyright Year
2015
Book
Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction

Print ISBN: 978-3-319-14898-4

Electronic ISBN: 978-3-319-14899-1

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-14899-1_10

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