Designing efficient fusion schemes for multimodal biometric systems using face and palmprint

Raghavendra, R.; Dorizzi, Bernadette; Rao, Ashok; Kumar, G. Hemantha

doi:10.1016/j.patcog.2010.11.008

Cited by 95 publications

(78 citation statements)

References 22 publications

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“…Furthermore, Eigen and Fisher always achieved 0% EER when fusing the palmprint with either the face or the fingerprint, regardless of the number of training samples. The results also confirmed Raghavendra et al (2011)'s assertion -the same feature transformation should be applied to different modalities.…”

Section: Preliminary Experiments For Developing the Guidelinessupporting

confidence: 81%

“…However, based on the biometric, specific regions contain less noise and more discriminatory information. Considering the entire image during fusion, rather than core regions, can lead to the phenomenon known as "the curse of dimensionality problem" (Raghavendra et al, 2011). These regions are typically centred around the core for alignment during image registration.…”

Section: Feature Selectionmentioning

confidence: 99%

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Feature-Fusion Guidelines for Image-Based Multi-Modal Biometric Fusion

Brown

Bradshaw

2017

SACJ

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The feature level, unlike the match score level, lacks multi-modal fusion guidelines. This work demonstrates a new approach for improved image-based biometric feature-fusion. The approach extracts and combines the face, fingerprint and palmprint at the feature level for improved human identification accuracy. Feature-fusion guidelines, proposed in our recent work, are extended by adding a new face segmentation method and the support vector machine classifier. The new face segmentation method improves the face identification equal error rate (EER) by 10%. The support vector machine classifier combined with the new feature selection approach, proposed in our recent work, outperforms other classifiers when using a single training sample. Feature-fusion guidelines take the form of strengths and weaknesses as observed in the applied feature processing modules during preliminary experiments. The guidelines are used to implement an effective biometric fusion system at the feature level, using a novel feature-fusion methodology, reducing the EER of two groups of three datasets namely: SDUMLA face, SDUMLA fingerprint and IITD palmprint; MUCT Face, MCYT Fingerprint and CASIA Palmprint.

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Section: Preliminary Experiments For Developing the Guidelinessupporting

confidence: 81%

Section: Feature Selectionmentioning

confidence: 99%

Feature-Fusion Guidelines for Image-Based Multi-Modal Biometric Fusion

Brown

Bradshaw

2017

SACJ

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“…In this respect, in [26], a user-specific weight strategy is used to compute the weighted sum of scores from different modalities. In general, the computation of weights is done based on the Equal Error Rate (EER), the distribution of scores, the quality of the individual biometrics or empirical schemes [5]. The Weighted Sum Rule (ws) of different score matchers can be calculated as:…”

Section: Match Score Level Fusionmentioning

confidence: 99%

Optimal Face-Iris Multimodal Fusion Scheme

Sharifi

Eskandari

2016

Symmetry

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Multimodal biometric systems are considered a way to minimize the limitations raised by single traits. This paper proposes new schemes based on score level, feature level and decision level fusion to efficiently fuse face and iris modalities. Log-Gabor transformation is applied as the feature extraction method on face and iris modalities. At each level of fusion, different schemes are proposed to improve the recognition performance and, finally, a combination of schemes at different fusion levels constructs an optimized and robust scheme. In this study, CASIA Iris Distance database is used to examine the robustness of all unimodal and multimodal schemes. In addition, Backtracking Search Algorithm (BSA), a novel population-based iterative evolutionary algorithm, is applied to improve the recognition accuracy of schemes by reducing the number of features and selecting the optimized weights for feature level and score level fusion, respectively. Experimental results on verification rates demonstrate a significant improvement of proposed fusion schemes over unimodal and multimodal fusion methods.

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“…. : (20) Once the multiresolution wavelet coefficients of each image acquired by each sensor are obtained, high-and lowfrequency components can be postprocessed using specific fusion rules, such as addition, or weighted averaging. Fig.…”

Section: A Multiwavelength Digital Holographymentioning

confidence: 99%