Incorporating image quality in multi-algorithm fingerprint verification

The final publication is available at Springer via http://dx.doi.org/10.1007/11608288_29Proceedings of International Conference, ICB 2006, Hong Kong (China)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.This work has been supported by Spanish MCYT TIC2003-08382-C05-01 and by European Commission IST-2002-507634 Biosecure NoE projects

Combining Biometric Evidence for Person Authentication

Humans are excellent experts in person recognition and yet they do not perform excessively well in recognizing others only based on one modality such as single facial image. Experimental evidence of this fact is reported concluding that even human authentication relies on multimodal signal analysis. The elements of automatic multimodal authentication along with system models are then presented. These include the machine experts as well as machine supervisors. In particular, fingerprint and speech based systems will serve as illustration. A signal adaptive supervisor based on the input biometric signal quality is evaluated

A principled approach to score level fusion in multimodal biometric systems

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

An On-Line Signature Verification System Based on Fusion of Local and Global Information

An on-line signature verification system exploiting both local and global information through decision-level fusion is presented. Globa

Fusion of Local and Regional Approaches for On-Line Signature Verification

Function-based methods for on-line signature verification are studied. These methods are classified into local and regional depending on the features used for matching. One representative method of each class is selected from the literature. The selected local and regional methods are based on Dynamic Time Warping and Hidden Markov Models, respectively. Some improvements are presented for the local method aimed at strengthening the performance against skilled forgeries. The two methods are compared following the protocol defined in the Signature Verification Competition 2004. Fusion results are also provided demonstrating the complementary nature of these two approaches