Multibiometric systems

Jain, Anil K.; Ross, Arun

doi:10.1145/962081.962102

Cited by 320 publications

(196 citation statements)

References 7 publications

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“…Biometric systems based on a single source of information (unimodal systems) suffer from limitations like the lack of uniqueness, non-universality and noisy data [2] and hence, may not be able to achieve the desired performance requirements of real-world applications. In contrast, multimodal biometric systems combine information from its component modalities to arrive at a decision [3].…”

Section: Introductionmentioning

confidence: 99%

A Principled Approach to Score Level Fusion in Multimodal Biometric Systems

Dass

Nandakumar

Jain

2005

Lecture Notes in Computer Science

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136

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Abstract. A multimodal biometric system integrates information from multiple biometric sources to compensate for the limitations in performance of each individual biometric system. We propose an optimal framework for combining the matching scores from multiple modalities using the likelihood ratio statistic computed using the generalized densities estimated from the genuine and impostor matching scores. The motivation for using generalized densities is that some parts of the score distributions can be discrete in nature; thus, estimating the distribution using continuous densities may be inappropriate. We present two approaches for combining evidence based on generalized densities: (i) the product rule, which assumes independence between the individual modalities, and (ii) copula models, which consider the dependence between the matching scores of multiple modalities. Experiments on the MSU and NIST multimodal databases show that both fusion rules achieve consistently high performance without adjusting for optimal weights for fusion and score normalization on a case-by-case basis.

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Section: Introductionmentioning

confidence: 99%

A Principled Approach to Score Level Fusion in Multimodal Biometric Systems

Dass

Nandakumar

Jain

2005

Lecture Notes in Computer Science

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136

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“…We also investigate the effects of varying image quality on a multi-algorithm approach [6] based on minutiae-and ridge-based matchers. These two matchers provide complementary information commonly exploited by score-level fusion [7,8].…”

Section: Introductionmentioning

confidence: 99%

Incorporating Image Quality in Multi-algorithm Fingerprint Verification

Fiérrez-Aguilar

Chen

Ortega‐Garcia

et al. 2005

Advances in Biometrics

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Esta es la versión de autor de la comunicación de congreso publicada en: This is an author produced version of a paper published in: Abstract. The effect of image quality on the performance of fingerprint verification is studied. In particular, we investigate the performance of two fingerprint matchers based on minutiae and ridge information as well as their score-level combination under varying fingerprint image quality. The ridge-based system is found to be more robust to image quality degradation than the minutiae-based system. We exploit this fact by introducing an adaptive score fusion scheme based on automatic quality estimation in the spatial frequency domain. The proposed scheme leads to enhanced performance over a wide range of fingerprint image quality.

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“…However, a single physical or behavioral characteristic of an individual can sometimes fail to be sufficient for identification. For this reason, multimodal biometric systems -i.e., systems that integrate two or more different biometric characteristics-are being developed to provide an acceptable performance, to increase the reliability of decisions, and to increase robustness to fraudulent technologies [17]. The biometric community puts a lot of effort into working on technical standards in the field of biometric fusion [18].…”

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A biometric identification system based on eigenpalm and eigenfinger features

Ribarić

Fratrić

2005

IEEE Trans. Pattern Anal. Machine Intell.

306

180

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Abstract-This paper presents a multimodal biometric identification system based on the features of the human hand. We describe a new biometric approach to personal identification using eigenfinger and eigenpalm features, with fusion applied at the matching-score level. The identification process can be divided into the following phases: capturing the image; preprocessing; extracting and normalizing the palm and strip-like finger subimages; extracting the eigenpalm and eigenfinger features based on the K-L transform; matching and fusion; and, finally, a decision based on the (k, l)-NN classifier and thresholding. The system was tested on a database of 237 people (1,820 hand images). The experimental results showed the effectiveness of the system in terms of the recognition rate (100 percent), the equal error rate (EER = 0.58 percent), and the total error rate (TER = 0.72 percent).

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Multibiometric systems

Abstract: The latest research indicates using a combination of biometric avenues for human identification is more effective, and far more challenging.

Cited by 320 publications

References 7 publications

A Principled Approach to Score Level Fusion in Multimodal Biometric Systems

A Principled Approach to Score Level Fusion in Multimodal Biometric Systems

Incorporating Image Quality in Multi-algorithm Fingerprint Verification

A biometric identification system based on eigenpalm and eigenfinger features

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