Intelligent Biometric Information Fusion using Support Vector Machine

doi:10.1007/978-3-540-38233-1_12

Book Chapter•DOI•

Intelligent Biometric Information Fusion using Support Vector Machine

Richa Singh¹, Mayank Vatsa¹, Afzel Noore¹•Institutions (1)

01 Jan 2007-pp 325-349

About: The article was published on 2007-01-01. It has received 27 citations till now. The article focuses on the topics: Support vector machine & Biometrics.

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Journal Article•DOI•

Integrated multilevel image fusion and match score fusion of visible and infrared face images for robust face recognition

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Richa Singh¹, Mayank Vatsa¹, Afzel Noore¹•Institutions (1)

West Virginia University¹

01 Mar 2008-Pattern Recognition

TL;DR: An integrated image fusion and match score fusion of multispectral face images using [email protected] SVM and Dezert Smarandache theory of fusion which is based on plausible and paradoxical reasoning is presented.

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176 citations

Cites background or methods from "Intelligent Biometric Information F..."

...In our previous research [12], we used 2ν-SVM for feature fusion, match score fusion, and expert fusion....
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...Further, several fusion algorithms have been proposed to fuse the information extracted from visible and LWIR face images at image level [8], [9], [10], [11], feature level [10], [11], [12], match score level [12], and decision level [12]....
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Proceedings Article•

EEG biometrics for individual recognition in resting state with closed eyes

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Daria La Rocca¹, Patrizio Campisi¹, G. Scarano²•Institutions (2)

Roma Tre University¹, Sapienza University of Rome²

27 Sep 2012

TL;DR: The resting state with closed eyes acquisition protocol has been here used and deeply investigated by varying the employed electrodes configuration both in number and location for optimizing the recognition performance still guaranteeing sufficient user convenience.

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Abstract: In this paper EEG signals are employed for the purpose of automatic user recognition. Specifically the resting state with closed eyes acquisition protocol has been here used and deeply investigated by varying the employed electrodes configuration both in number and location for optimizing the recognition performance still guaranteeing sufficient user convenience. A database of 45 healthy subjects has been employed in the analysis. Autoregressive stochastic modeling and polynomial regression based classification has been applied to extracted brain rhythms in order to identify the most distinctive contributions of the different subbands in the recognition process. Our analysis has shown that significantly high recognition rates, up to 98.73%, can be achievedwhen using proper triplets of electrodes,which cannot be achieved by employing couple of electrodes,whereas sets of five electrodes in the central posterior region of the scalp can guarantee very high recognition performance while limiting user convenience.

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59 citations

Proceedings Article•

Forensic biometrics: From two communities to one discipline

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Didier Meuwly¹, Raymond N.J. Veldhuis²•Institutions (2)

Netherlands Forensic Institute¹, University of Twente²

27 Sep 2012

TL;DR: How the fields of biometrics and forensic science can contribute and benefit from each other is described to foster the development of new methods and tools improving the current forensic biometric applications and allowing for the creation of new ones.

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Abstract: This article describes how the fields of biometrics and forensic science can contribute and benefit from each other. The aim is to foster the development of new methods and tools improving the current forensic biometric applications and allowing for the creation of new ones. The article begins with a definition and a summary of the development in forensic biometrics. Then it describes the data and biometric modalities of interest in forensic science and the forensic applications embedding biometric technology. On this basis it describes the solutions and limitations of the current practice regarding the data, the technology and the inference models. Finally, it proposes research orientations for the improvement of the current forensic biometric applications and suggests some ideas for the development of some new forensic biometric applications

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57 citations

Patent•

Score fusion and training data recycling for video classification

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Jingchen Liu¹, Scott McCloskey¹•Institutions (1)

Honeywell¹

18 Sep 2012

TL;DR: In this article, a local expert forest model for meta-level score fusion for event detection is proposed, where performance variations of classifiers in different regions of a score space can be adapted.

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Abstract: Multiple classifiers can be applied independently to evaluate images or video. Where there are heavily imbalanced class distributions, a local expert forest model for meta-level score fusion for event detection can be used. Performance variations of classifiers in different regions of a score space can be adapted. Multiple pairs of experts based on different partitions, or “trees,” can form a “forest,” balancing local adaptivity and over-fitting. Among ensemble learning methods, stacking with a meta-level classifier can be used to fuse an output of multiple base-level classifiers to generate a final score. A knowledge-transfer framework can reutilize the base-training data for learning the meta-level classifier. By recycling the knowledge obtained during a base-classifier-training stage, efficient use can be made of all available information, such as can be used to achieve better fusion and better overall performance.

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43 citations

Posted Content•

Unification of Evidence Theoretic Fusion Algorithms: A Case Study in Level-2 and Level-3 Fingerprint Features

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Mayank Vatsa¹, Richa Singh¹, Afzel Noore¹•Institutions (1)

West Virginia University¹

01 Jun 2017-viXra

TL;DR: This paper formulates an evidence-theoretic multimodal unification approach using belief functions that take into account the variability in biometric image characteristics that is computationally efficient, and the verification accuracy is not compromised even when conflicting decisions are encountered.

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Abstract: This paper formulates an evidence-theoretic multimodal unification approach using belief functions that takes into account the variability in biometric image characteristics. While processing non-ideal images the variation in the quality of features at different levels of abstraction may cause individual classifiers to generate conflicting genuine-impostor decisions.

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41 citations

Cites background or methods from "Intelligent Biometric Information F..."

...In our previous research [5], we found that for multimodal fusion, 2ν-SVM provides better classification with lower time complexity compared to the classical SVM [20]....
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...Thus, verification based on unimodal biometric systems is not always reliable, and researchers have shown that the fusion of multiple biometric modalities generally provides higher verification performance [2]–[5]....
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...Existing biometric fusion algorithms such as sum rule [4] and support vector machine (SVM) fusion [5] yield good performance for some applications or under certain conditions but not universally for all scenarios....
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...In the literature, there are different forms of biometric information fusion [2], [4], [5]: single biometric–multiple representation, single biometric– multiple matchers, multiple biometrics–multiple representa-...
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Intelligent Biometric Information Fusion using Support Vector Machine

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