Fusion of audio and video information for multi modal person authentication

Duc, Benoît; Bigün, Elizabeth Saers; Bigün, Josef; Maître, Gilbert; Fischer, Stefan

doi:10.1016/s0167-8655(97)00071-8

Cited by 59 publications

(24 citation statements)

References 4 publications

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“…Another approach is the work of Bigun et al [22]; here the fusion scheme (the controller as the authors say) is designed within a Bayesian probabilistic framework; as before opinions from each classiÿer (i.e., from each expert) are nonlinearly normalized; then an overall decision is reached by thresholding the posterior probability of a correct choice. Results are compared with a thresholded arithmetic mean of the opinions of each expert.…”

Section: Multimodal Bissmentioning

confidence: 99%

Biometric identification systems

et al. 2003

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In this paper, we provide an overview of the fundamentals of biometric identiÿcation, together with a description of the main biometric technologies currently in use, all of them within a common reference framework. A comparison on di erent qualitative parameters of these technologies is also given, so that the reader may have a clear perspective of advantages and disadvantages of each. A section on multibiometrics describes the state of the art in making these systems work coordinately. Fusion at di erent conceptual levels is described. Finally, a section on commercial issues provides the reader a perspective of the main companies currently involved in this ÿeld. ?

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Section: Multimodal Bissmentioning

confidence: 99%

Biometric identification systems

et al. 2003

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“…Let the Matrix be D and the sequences are A, B with lengths M, N respectively. Then D is calculated using equation (12). Calculate the DTW distance values separately for VPP and HPP vectors from all the users for all the training images to the testing image and obtain distances from each user using average distance method.…”

Section: 667%mentioning

confidence: 99%

“…There are several multimodal biometric person authentication systems developed in the literature [2][3][4][5][6][7][8][9][10][11][12]. Person authentication based on speech and face features, is one of the first multimodal biometric system [3].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multimodal Biometric Person Authentication using Speech, Signature and Handwriting Features

Eshwarappa¹,

Latte²

2011

SpecialIssue

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Abstract-The objective of this work is to develop a multimodal biometric system using speech, signature and handwriting information. Unimodal biometric person authentication systems are initially developed for each of these biometric features. Methods are then explored for integrating them to obtain multimodal system. Apart from implementing state-of-the art systems, the major part of the work is on the new explorations at each level with the objective of improving performance and robustness. The latest research indicates multimodal person authentication system is more effective and more challenging. This work demonstrates that the fusion of multiple biometrics helps to minimize the system error rates. As a result, the identification performance is 100% and verification performances, False Acceptance Rate (FAR) is 0%, and False Rejection Rate (FRR) is 0%.

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“…Many biometric authentication protocols are computationally intensive and can well-utilize the emerging computational capabilities of low-power mobile devices. A broad range of biomedical data, from physiological signals/images to behavioral traits, has been explored for its biometric authentication and identification potential (Biel, et al, 2001;Chan, et al, 2008;Doi & Yamanaka, 2004;Duc, et al, 1997;Elsherief, et al, 2006;Faundez-Zanuy, 2005;Irvine, et al, 2001;Israel, et al, 2005;Shen, et al, 2002;Sullivan, et al, 2007;Yao & Wan, 2010;G. H. Zhang, et al, 2009).…”

Section: Introductionmentioning

confidence: 99%