Biometric Fusion Using Enhanced SVM Classification

Fahmy, Mahmoud; Atyia, Amir; Elfouly, Raafat

doi:10.1109/iih-msp.2008.66

Cited by 20 publications

(9 citation statements)

References 5 publications

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“…For instance, Fahmy et al . [40] proposed to use the support vector machine (SVM) as a fusion tool for iris and fingerprint multi‐biometric system. While Bayesian inference based fingerprint, face and on‐line signature fusion system was presented in [41].…”

Section: Related Workmentioning

confidence: 99%

Weighted quasi‐arithmetic mean based score level fusion for multi‐biometric systems

Herbadji¹,

Guermat²,

Ziet

et al. 2020

IET biom.

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Biometrics is now being principally employed in many daily applications ranging from the border crossing to mobile user authentication. In the high-security scenarios, biometrics require stringent accuracy and performance criteria. Towards this aim, multi-biometric systems that fuse the evidences from multiple sources of biometric have exhibited to diminish the error rates and alleviate inherent frailties of the individual biometric systems. In this article, a novel scheme for score-level fusion based on weighted quasi-arithmetic mean (WQAM) has been proposed. Specifically, WQAMs are estimated via different trigonometric functions. The proposed fusion scheme encompasses properties of both weighted mean and quasi-arithmetic mean. Moreover, it does not require any leaning process. Experimental results on three publicly available data sets (i.e. NIST-BSSR1 Multimodal, NIST-BSSR1 Fingerprint and NIST-BSSR1 Face) for multi-modal, multi-unit and multi-algorithm systems show that presented WQAM fusion algorithm outperforms the previously proposed score fusion rules based on transformation (e.g. t-norms), classification (e.g. support vector machines) and density estimation (e.g. likelihood ratio) methods.

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Section: Related Workmentioning

confidence: 99%

Weighted quasi‐arithmetic mean based score level fusion for multi‐biometric systems

Herbadji¹,

Guermat²,

Ziet

et al. 2020

IET biom.

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“…A support vector machine (SVM) is a classifier that is used to analyze data and recognize patterns, used for classification and regression analysis. The standard SVM takes a set of input data and predicts, for each given input, which of two possible classes that is person is Genuine or Imposter [18]. The Back Propagation neural network (BPNN) belong to the multi-layers feed-forward neural networks, which have one input layer, one output layer, and one or more hidden layers.…”

Section: Literature Surveymentioning

confidence: 99%

Adaptive Cascade Classifier based Multimodal Biometric Recognition and Identification System

Gawande¹,

Hajari²

2013

IJAIS

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Biometrics consists of techniques for uniquely recognizing humans based upon one or more intrinsic physical or behavioral traits such as Iris, fingerprint, Face and Palm geometry etc. To overcome the limitations of Unimodal biometric system, a multimodal biometric is proposed. Amongst the various fusion levels, feature level fusion is expected to offer better recognition. Feature level fusion fused the extracted feature obtained from biometric traits. The proposed system is based on feature level fusion and adaptive cascade classifier for precise and reliable multimodal recognition and identification. Verification of Genuine and imposter individual classification is done using Backpropagation neural network. The simulation results demonstrated that a multibiometric template provides better recognition performance compared to a unibiometric template and adaptive cascade classification system significantly outperforms single classifier. General TermsAdaptive Cascade Classifier, Feature level fusion.

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“…Also score level fusion of iris and fingerprint biometrics was implemented using Support Vector Machine [5]. Kittler et.…”

Section: Introductionmentioning

confidence: 99%

Differential evolution based score level fusion for multi-modal biometric systems

Mukherjee

Pal

Majumder

et al. 2014

2014 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM)

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The purpose of a multimodal biometric system is to construct a robust classifier of genuine and imposter candidates by extracting useful information from several biometric sources which fail to perform well in identification or verification as individual biometric systems. Amongst different levels of information fusion, very few approaches exist in literature exploring score level fusion. In this paper, we propose a novel adaptive weight and exponent based function mapping the matching scores from different biometric sources into a single amalgamated matching score to be used by a classifier for further decision making. Differential Evolution (DE) has been employed to adjust these tunable parameters with the objective being the minimization of the overlapping area of the frequency distributions of genuine and imposter scores in the fused score space, which are estimated by Gaussian kernel density method to achieve higher level of accuracy. Experimental results show that, the proposed method outperforms the conventional score-level fusion rules (sum, product, tanh, exponential) when tested on two databases of 4 modalities (fingerprint, iris, left ear and right ear) of 200 and 516 users and thus confirms the effectiveness of score level fusion. The preliminary results provide adequate motivation towards future research in the line of the application of meta-heuristics in score level fusion.

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Biometric Fusion Using Enhanced SVM Classification

Cited by 20 publications

References 5 publications

Weighted quasi‐arithmetic mean based score level fusion for multi‐biometric systems

Weighted quasi‐arithmetic mean based score level fusion for multi‐biometric systems

Adaptive Cascade Classifier based Multimodal Biometric Recognition and Identification System

Differential evolution based score level fusion for multi-modal biometric systems

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