Person identification using multiple cues

Brunelli, Roberto; Falavigna, Daniele

doi:10.1109/34.464560

Cited by 498 publications

(235 citation statements)

References 18 publications

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“…Brunelli and Falavigna [9] propose two different methods for combining ÿve opinions. Since the source of the opinions (scores, in the author's terminology) are fairly di erent (two speech features and three image features) a common reference framework is needed prior to fusion.…”

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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“…Examples are global-learning-global-decision (GG) (Brunelli and Falavigna, 1995;Bigun et al, 1997;Kittler et al, 1998;Hong and Jain, 1998;Ben-Yacoub et al, 1999;Chatzis et al, 1999;Verlinde et al, 2000), local-learning-globaldecision (LG) (Jain and Ross, 2002;Kumar and Zhang, 2003;Indovina et al, 2003;Fierrez-Aguilar et al, 2004;Wang et al, 2004;Toh et al, 2004;Poh and Bengio, 2005), and similarly global-learninglocal-decision (GL) (Jain and Ross, 2002;Toh et al, 2004), and local-learninglocal-decision (LL) (Toh et al, 2004). In the present work we adhere to this taxonomy and extend it by incorporating new items: adapted-learning and adapted-decisions.…”

Section: Related Work and Motivationmentioning

confidence: 99%

“…A common practice in most of the reported works on multimodal biometrics is to combine the matching scores obtained from the unimodal systems by using simple rules (e.g., sum, product), statistical methods, or machine learning procedures (Brunelli and Falavigna, 1995;Bigun et al, 1997;Kittler et al, 1998;Hong and Jain, 1998;Ben-Yacoub et al, 1999;Chatzis et al, 1999;Verlinde et al, 2000). A remarkable characteristic of this approach, as compared to the feature-level combination techniques, is the possibility of designing structured multimodal systems by using existing unimodal recognition strategies (Maltoni et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

Adapted user-dependent multimodal biometric authentication exploiting general information

Fiérrez-Aguilar

Garcia‐Romero

Ortega‐Garcia

et al. 2005

Pattern Recognition Letters

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This is the author’s version of a work that was accepted for publication in Pattern Recognition Letters. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition Letters 26.16 (2005): 2628 – 2639, DOI: 10.1016/j.patrec.2005.06.008A novel adapted strategy for combining general and user-dependent knowledge at the decision-level in multimodal biometric authentication is presented. User- independent, user-dependent, and adapted fusion and decision schemes are com- pared by using a bimodal system based on ¯ngerprint and written signature. The adapted approach is shown to outperform the other strategies considered in this pa- per. Exploiting available information for training the fusion function is also shown to be better than using existing information for post-fusion trained decisions.This work has been supported by the Spanish Ministry for Science and Tech- nology under projects TIC2003-09068-C02-01 and TIC2003-08382-C05-01

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“…To combine the local matching scores into an overall decision, a common approach is to use a pre-defined voting space (e.g. [16][17][18][19][20][21]). This approach works for matched training and testing, but less so for the presence of mismatches between the training and testing features.…”

Section: Introductionmentioning

confidence: 99%

Robust face recognition using posterior union model based neural networks

Lin

Crookes

2009

IET Comput. Vis.

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Face recognition with unknown, partial distortion and occlusion is a practical problem, and has a wide range of applications, including security and multimedia information retrieval. The authors present a new approach to face recognition subject to unknown, partial distortion and occlusion. The new approach is based on a probabilistic decision-based neural network, enhanced by a statistical method called the posterior union model (PUM). PUM is an approach for ignoring severely mismatched local features and focusing the recognition mainly on the reliable local features. It thereby improves the robustness while assuming no prior information about the corruption. We call the new approach the posterior union decision-based neural network (PUDBNN). The new PUDBNN model has been evaluated on three face image databases (XM2VTS, AT&T and AR) using testing images subjected to various types of simulated and realistic partial distortion and occlusion. The new system has been compared to other approaches and has demonstrated improved performance.

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Person identification using multiple cues

Cited by 498 publications

References 18 publications

Biometric identification systems

Biometric identification systems

Adapted user-dependent multimodal biometric authentication exploiting general information

Robust face recognition using posterior union model based neural networks

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