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Book ChapterDOI

Intelligent Biometric Information Fusion using Support Vector Machine

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.
Citations
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Journal ArticleDOI
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.

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
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.
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.

59 citations

Proceedings Article
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.
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

57 citations

Patent
Jingchen Liu1, Scott McCloskey1
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.
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.

43 citations

Posted Content
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.
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.

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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Proceedings ArticleDOI
10 Dec 2002
TL;DR: It is demonstrated that the performance of multibiometric systems can be further improved by learning user-specific parameters, and thresholds and weights used to indicate the importance of matching scores output by each biometric trait are considered.
Abstract: Biometric systems that use a single biometric trait have to contend with noisy data, restricted degrees of freedom, failure-to-enroll problems, spoof attacks, and unacceptable error rates. Multibiometric systems that use multiple traits of an individual for authentication, alleviate some of these problems while improving verification performance. We demonstrate that the performance of multibiometric systems can be further improved by learning user-specific parameters. Two types of parameters are considered here. (i) Thresholds that are used to decide if a matching score indicates a genuine user or an impostor, and (ii) weights that are used to indicate the importance of matching scores output by each biometric trait. User-specific thresholds are computed using the cumulative histogram of impostor matching scores corresponding to each user. The user-specific weights associated with each biometric are estimated by searching for that set of weights which minimizes the total verification error. The tests were conducted on a database of 50 users who provided fingerprint, face and hand geometry data, with 10 of these users providing data over a period of two months. We observed that user-specific thresholds improved system performance by /spl sim/ 2%, while user-specific weights improved performance by /spl sim/ 3%.

266 citations


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

  • ...Researchers have also proposed several other algorithms [2, 3 , 7, 8] for fusion at match score level and decision level....

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Journal ArticleDOI
TL;DR: This work uses the logistic transform to integrate the output scores from three different fingerprint matching algorithms and confirms the effectiveness of the proposed integration scheme on a large fingerprint database.

238 citations


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

  • ...Various researchers have suggested that no single biometric modality can provide the protection required for high security applications [1] - [ 4 ]....

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Book ChapterDOI
12 Mar 1997
TL;DR: By using real data coming from a person authentication system using image and speech data, it is confirmed that the proposed supervisor improves the quality of individual expert decisions by reaching success rates of 99.5 %.
Abstract: We present an algorithm functioning as a supervisor module in a multi expert decision making machine. It uses the Bayes theory in order to estimate the biases of individual expert opinions. These are then used to calibrate and conciliate expert opinions to one opinion. We present a framework for simulating decision strategies using expert opinions whose properties are easily modifiable. By using real data coming from a person authentication system using image and speech data we were able to confirm that the proposed supervisor improves the quality of individual expert decisions by reaching success rates of 99.5 %.

178 citations

Journal ArticleDOI
01 Nov 1999
TL;DR: Two modifications of the FKM and FVQ algorithms, based on a fuzzy vector distance definition, are proposed to handle the fuzzy data and utilize the quality measure, and the use of the quality via the proposed modified algorithms increases the performance of the fusion system.
Abstract: The use of clustering algorithms for decision-level data fusion is proposed. Person authentication results coming from several modalities (e.g., still image, speech), are combined by using fuzzy k-means (FKM) and fuzzy vector quantization (FVQ) algorithms, and a median radial basis function (MRBF) network. The quality measure of the modalities data is used for fuzzification. Two modifications of the FKM and FVQ algorithms, based on a fuzzy vector distance definition, are proposed to handle the fuzzy data and utilize the quality measure. Simulations show that fuzzy clustering algorithms have better performance compared to the classical clustering algorithms and other known fusion algorithms. MRBF has better performance especially when two modalities are combined. Moreover, the use of the quality via the proposed modified algorithms increases the performance of the fusion system.

160 citations


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

  • ...Fusion Algorithm Algorithm [40] Algorithm Algorithm [ 15 ]...

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  • ...Several other researchers proposed algorithms based on Support Vector Machine (SVM) [14], fuzzy clustering [ 15 ] and radial basis neural network [6, 16] to fuse the information at different levels....

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  • ...• Who are the experts and how reliable are the experts? • How does an administrator make the decision based on the opinions rendered by different experts? • How do we normalize the data? In [ 15 , 17, 41, 42], detailed experimental evaluations of multiple expert fusion algorithms are presented....

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  • ...comparison among the proposed algorithms and the algorithms presented in [ 15 ] and [40]....

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  • ...In this subsection, we compare the three proposed learning based fusion algorithms with the algorithms presented in [ 15 ] and [40]....

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01 Jan 1994
TL;DR: LTS1 Reference LTS-ARTICLE-1994-002 Record created on 2006-06-14, modified on 2016-08-08.
Abstract: Keywords: LTS1 Reference LTS-ARTICLE-1994-002 Record created on 2006-06-14, modified on 2016-08-08

156 citations