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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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 2012TL;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 2012TL;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•
18 Sep 2012TL;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•
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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TL;DR: It is found that recognition performance is not significantly different between the face and the ear, for example, 70.5 percent versus 71.6 percent in one experiment and multimodal recognition using both the ear and face results in statistically significant improvement over either individual biometric.
Abstract: Researchers have suggested that the ear may have advantages over the face for biometric recognition. Our previous experiments with ear and face recognition, using the standard principal component analysis approach, showed lower recognition performance using ear images. We report results of similar experiments on larger data sets that are more rigorously controlled for relative quality of face and ear images. We find that recognition performance is not significantly different between the face and the ear, for example, 70.5 percent versus 71.6 percent, respectively, in one experiment. We also find that multimodal recognition using both the ear and face results in statistically significant improvement over either individual biometric, for example, 90.9 percent in the analogous experiment.
597 citations
"Intelligent Biometric Information F..." refers background in this paper
...Ross [9], Kumar [10], and Singh [11] proposed the feature level fusion algorithm, Chang [ 12 ] and Singh [11] proposed algorithm for image level fusion....
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TL;DR: Experimental results on the image dataset from 100 users confirm the utility of hand geometry features with those from palmprints and achieve promising results with a simple image acquisition setup.
Abstract: A new approach for the personal identification using hand images is presented This paper attempts to improve the performance of palmprint-based verification system by integrating hand geometry features Unlike other bimodal biometric systems, the users does not have to undergo the inconvenience of passing through two sensors since the palmprint and hand geometry features can be are acquired from the same image, using a digital camera, at the same time Each of these gray level images are aligned and then used to extract palmprint and hand geometry features These features are then examined for their individual and combined performance The image acquisition setup used in this work was inherently simple and it does not employ any special illumination nor does it use any pegs to cause any inconvenience to the users Our experimental results on the image dataset from 100 users confirm the utility of hand geometry features with those from palmprints and achieve promising results with a simple image acquisition setup
537 citations
"Intelligent Biometric Information F..." refers background in this paper
...Ross [9], Kumar [ 10 ], and Singh [11] proposed the feature level fusion algorithm, Chang [12] and Singh [11] proposed algorithm for image level fusion....
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TL;DR: Differences and similarities are shown between this mean combination rule and the product combination rule in theory and in practice.
417 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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TL;DR: In this article, the main ideas of statistical learning theory, support vector machines (SVMs), and kernel feature spaces are briefly described, with particular emphasis on a description of the so-called ν-SVM.
Abstract: We briefly describe the main ideas of statistical learning theory, support vector machines (SVMs), and kernel feature spaces. We place particular emphasis on a description of the so-called ν-SVM, including details of the algorithm and its implementation, theoretical results, and practical applications. Copyright © 2005 John Wiley & Sons, Ltd.
410 citations
28 Mar 2005
TL;DR: This work discusses fusion at the feature level in 3 different scenarios: (i) fusion of PCA and LDA coefficients of face; (ii) Fusion of LDA coefficient corresponding to the R,G,B channels of a face image; and (iii) fusionof face and hand modalities.
Abstract: Multibiometric systems utilize the evidence presented by multiple biometric sources (e.g., face and fingerprint, multiple fingers of a user, multiple matchers, etc.) in order to determine or verify the identity of an individual. Information from multiple sources can be consolidated in several distinct levels, including the feature extraction level, match score level and decision level. While fusion at the match score and decision levels have been extensively studied in the literature, fusion at the feature level is a relatively understudied problem. In this paper we discuss fusion at the feature level in 3 different scenarios: (i) fusion of PCA and LDA coefficients of face; (ii) fusion of LDA coefficients corresponding to the R,G,B channels of a face image; (iii) fusion of face and hand modalities. Preliminary results are encouraging and help in highlighting the pros and cons of performing fusion at this level. The primary motivation of this work is to demonstrate the viability of such a fusion and to underscore the importance of pursuing further research in this direction.
397 citations
"Intelligent Biometric Information F..." refers background or methods in this paper
...In [ 9 ], feature fusion is performed by concatenating two feature vectors....
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...Feature fusion in biometrics has been addressed by several researchers [ 9 , 11] in the literature....
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...The ROC plot also shows that the performance of proposed learning based feature fusion algorithm is better than the existing feature fusion algorithm [ 9 ]....
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...Ross [ 9 ], Kumar [10], and Singh [11] proposed the feature level fusion algorithm, Chang [12] and Singh [11] proposed algorithm for image level fusion....
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