Intelligent Biometric Information Fusion using Support Vector Machine

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

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Intelligent Biometric Information Fusion using Support Vector Machine

Book Chapter•DOI•

Intelligent Biometric Information Fusion using Support Vector Machine

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

West Virginia University¹

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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References

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Journal Article•DOI•

Score normalization in multimodal biometric systems

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Anil K. Jain¹, Karthik Nandakumar¹, Arun Ross²•Institutions (2)

Michigan State University¹, West Virginia University²

01 Dec 2005-Pattern Recognition

TL;DR: Study of the performance of different normalization techniques and fusion rules in the context of a multimodal biometric system based on the face, fingerprint and hand-geometry traits of a user found that the application of min-max, z-score, and tanh normalization schemes followed by a simple sum of scores fusion method results in better recognition performance compared to other methods.

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2,021 citations

"Intelligent Biometric Information F..." refers methods in this paper

...The details of normalization in multimodal biometrics are described in [ 39 ]....
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Journal Article•DOI•

Decision combination in multiple classifier systems

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Tin Kam Ho¹, Jonathan J. Hull¹, Sargur N. Srihari¹•Institutions (1)

State University of New York System¹

01 Jan 1994-IEEE Transactions on Pattern Analysis and Machine Intelligence

TL;DR: This work proposes three methods based on the highest rank, the Borda count, and logistic regression for class set reranking that have been tested in applications of degraded machine-printed characters and works from large lexicons, resulting in substantial improvement in overall correctness.

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Abstract: A multiple classifier system is a powerful solution to difficult pattern recognition problems involving large class sets and noisy input because it allows simultaneous use of arbitrary feature descriptors and classification procedures. Decisions by the classifiers can be represented as rankings of classifiers and different instances of a problem. The rankings can be combined by methods that either reduce or rerank a given set of classes. An intersection method and union method are proposed for class set reduction. Three methods based on the highest rank, the Borda count, and logistic regression are proposed for class set reranking. These methods have been tested in applications of degraded machine-printed characters and works from large lexicons, resulting in substantial improvement in overall correctness. >

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1,703 citations

Journal Article•DOI•

Information fusion in biometrics

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Arun Ross¹, Anil K. Jain¹•Institutions (1)

Michigan State University¹

01 Sep 2003-Pattern Recognition Letters

TL;DR: This paper addresses the problem of information fusion in biometric verification systems by combining information at the matching score level by combining three biometric modalities (face, fingerprint and hand geometry).

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1,611 citations

"Intelligent Biometric Information F..." refers background or methods in this paper

...Different forms of biometric information fusion are: single biometric - multiple representation, single biometric - multiple matchers, and multiple biometrics [ 2 ]....
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...It has been suggested that the fusion of match scores of two or more classifiers gives better performance over a single classifier [1, 2 ]. In general, match score fusion is performed using sum rule, product rule or other statistical rules....
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...Researchers have also proposed several other algorithms [ 2 , 3, 7, 8] for fusion at match score level and decision level....
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...To alleviate this problem and enhance the performance of a biometric system, information from different biometric sources are combined and such systems are known as multimodal biometric systems [ 2 ]....
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...Thus biometric information can be combined at different levels such as, image level fusion, feature level fusion, match score level fusion, expert or decision level fusion, and rank level fusion [ 2 ]....
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Journal Article•DOI•

Person identification using multiple cues

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Roberto Brunelli, Daniele Falavigna

01 Oct 1995-IEEE Transactions on Pattern Analysis and Machine Intelligence

TL;DR: A novel technique for the integration of multiple classifiers at an hybrid rank/measurement level is introduced using HyperBF networks and two different methods for the rejection of an unknown person are introduced.

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Abstract: This paper presents a person identification system based on acoustic and visual features. The system is organized as a set of non-homogeneous classifiers whose outputs are integrated after a normalization step. In particular, two classifiers based on acoustic features and three based on visual ones provide data for an integration module whose performance is evaluated. A novel technique for the integration of multiple classifiers at an hybrid rank/measurement level is introduced using HyperBF networks. Two different methods for the rejection of an unknown person are introduced. The performance of the integrated system is shown to be superior to that of the acoustic and visual subsystems. The resulting identification system can be used to log personal access and, with minor modifications, as an identity verification system. >

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663 citations

Journal Article•DOI•

Integrating faces and fingerprints for personal identification

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Lin Hong¹, Anil K. Jain¹•Institutions (1)

Michigan State University¹

01 Dec 1998-IEEE Transactions on Pattern Analysis and Machine Intelligence

TL;DR: A prototype biometrics system which integrates faces and fingerprints is developed which overcomes the limitations of face recognition systems as well as fingerprint verification systems and operates in the identification mode with an admissible response time.

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Abstract: An automatic personal identification system based solely on fingerprints or faces is often not able to meet the system performance requirements. We have developed a prototype biometrics system which integrates faces and fingerprints. The system overcomes the limitations of face recognition systems as well as fingerprint verification systems. The integrated prototype system operates in the identification mode with an admissible response time. The identity established by the system is more reliable than the identity established by a face recognition system. In addition, the proposed decision fusion scheme enables performance improvement by integrating multiple cues with different confidence measures. Experimental results demonstrate that our system performs very well. It meets the response time as well as the accuracy requirements.

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651 citations