Speech &amp;amp; face based biometric authentication at IDIAP

Sanderson, Conrad; Bengio, Samy; Bourlard, Hervé; Mariéthoz, Johnny; Collobert, Ronan; BenZeghiba, Mohamed Faouzi; Cardinaux, Fabien; Marcel, Sébastien

doi:10.1109/icme.2003.1221233

Cited by 6 publications

(1 citation statement)

References 33 publications

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“…This system uses natural fusion approach as both of the biometric features originate from the same part of the body. Apart from that, unlike the other multimodal biometric system that required multiple input devices [5], only a single image capturing device is needed in this system. With this, the users do not need to go through the inconvenience of using several different acquiring devices for security access.…”

Section: Introductionmentioning

confidence: 99%

A single-sensor hand geometry and palmprint verification system

Goh

Connie

Jin

et al. 2003

Proceedings of the 2003 ACM SIGMM Workshop on Biometrics Methods and Applications - WBMA '03

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Several contributions have shown that fusion of decisions or scores obtained from various single-modal biometrics verification systems often enhances the overall system performance. A recent approach of multimodal biometric systems with the use of single sensor has received significant attention among researchers. In this paper, a combination of hand geometry and palmprint verification system is being developed. This system uses a scanner as sole sensor to obtain the hands images. First, the hand geometry verification system performs the feature extraction to obtain the geometrical information of the fingers and palm. Second, the region of interest (ROI) is detected and cropped by palmprint verification system. This ROI acts as the base for palmprint feature extraction by using Linear Discriminant Analysis (LDA). Lastly, the matching scores of the two individual classifiers is fused by several fusion algorithms namely sum rule, weighted sum rule and Support Vector Machine (SVM). The results of the fusion algorithms are being compared with the outcomes of the individual palm and hand geometry classifiers. We are able to show that fusion using SVM with Radial Basis Function (RBF) kernel has outperformed other combined and individual classifiers.

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Section: Introductionmentioning

confidence: 99%

A single-sensor hand geometry and palmprint verification system

Goh

Connie

Jin

et al. 2003

Proceedings of the 2003 ACM SIGMM Workshop on Biometrics Methods and Applications - WBMA '03

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Biometric Identity Verification using Automatic Speaker Recognition

Sujatha

Rao

Rao³

et al. 2015

2015 International Conference on Electrical, Electronics, Signals, Communication and Optimization (EESCO)

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Biometric recognition by using audio and visual feature fusion

Huang

2012

2012 International Conference on System Science and Engineering (ICSSE)

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this paper proposes an algorithm to fuse feature information of audio and visual biometrics, and a framework of Probabilistic Neural Network (PNN) is developed to achieve feature fusion. In addition, an Audio-Visual based TextIndependent (AVTI) person recognition system has been constructed by using still visual images and text-independent audio signals. Test results show that the proposed algorithm achieves a better recognition rate, and it is superior to any single modal biometrics involved in the system. The results also indicate that the proposed method overcomes the problems of different frame rates of audio and visual signals and the curse of dimensionality normally existed in feature fusion procedure.

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Speech & face based biometric authentication at IDIAP

Cited by 6 publications

References 33 publications

A single-sensor hand geometry and palmprint verification system

A single-sensor hand geometry and palmprint verification system

Biometric Identity Verification using Automatic Speaker Recognition

Biometric recognition by using audio and visual feature fusion

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