Feature extraction for different distances of visible reflection iris using multiscale sparse representation of local Radon transform

Security systems using one identification tool are not ideal. Multisystem security, which using two or more types of security levels like for example using identification password and card, can increase the security of a system, however it is not an ideal security system. Password maybe hacked or forgotten, and Identification card is something we have and could be stolen. This chapter proposes a cascaded multimodal biometric system using fingerprint and iris recognition based on minutiae extraction for fingerprint identification and encoding the log-Gabor filtering for iris recognition. The experiments compare FAR, FRR, and accuracy evaluation metrics for a unimodal biometric system based on either fingerprint or iris and the cascaded multimodal biometric system that sequentially utilizes the fingerprint and iris traits. The proposed system has FAR = 0, FRR = 0.057, and accuracy 99.86%. The results show the superior performance of the proposed multimodal system compared to the unimodal system.

Multimodal Biometric Personal Identification and Verification

Biometric cryptosystems and cancelable biometrics offer several advantages over generic biometric systems. The most important advantages are summarized in Table 15.1. These major advantages over conventional biometric systems call for several applications. In order to underline the potential of both technologies two essential use cases are discussed in detail. With respect to the design goals, biometric cryptosystems and cancelable biometrics offer significant advantages to enhance the privacy and security of biometric systems, providing reliable biometric authentication at a high security level. Techniques which provide provable security/privacy, while achieving practical recognition rates have remained elusive (even on small datasets).

Advances, Applications, and Challenges

Cancelable biometrics consist of intentional, repeatable distortions of biometric signals based on transforms which provide a comparison of biometric templates in the transformed domain [418].

Cancelable Iris Biometrics

This paper describes a method of gait recognition using multiple gait features in conjunction with score-level fusion techniques. More specifically, we focus on the state-of-the-art period-based gait features such as a gait energy image, a frequency-domain feature, a gait entropy image, a chrono-gait image, and a gait flow image. In addition, we employ various types of the score-level fusion approaches including not only conventional transformation-based approaches (e.g., sum-rule and min-rule) but also classification-based approaches (e.g., support vector machine) and density-based approaches (e.g., Gaussian mixture model, kernel density estimation, linear logistic regression). In experiments, the large-population gait database with 3,249 subjects was used to measure the performance improvement in a statistically reliable way. The experimental results show 7% relative improvement on average with regard to equal error rate of the false acceptance rate and false rejection rate in verification scenarios, and also show 20% reduction of the number of candidates to be checked under 1% misdetection rate on average in screening tasks.

/pdf/on-combining-gait-features-2ipzl0zxmp.pdf

On combining gait features

Fusion of multiple algorithms for biometric verification performance improvement has received considerable attention. This paper proposes an iris recognition method based on multialgorithmic fusion. The proposed method combines the phase information based algorithm and zero-crossing representation based algorithm at the matching score level. The fusion rule based on support vector machine (SVM) is applied to generate a fused score which is used to make the fial decision. The experimental results on CASIA and UBIRIS iris image databases show that the proposed multialgorithmic fusion method can bring obvious performance improvement compared with any single algorithm, and the results also demonstrate that the fusion rule based on SVM can achieve better performance than conventional 1 fusion rules.

Iris recognition based on multialgorithmic fusion

Fusion of multiple instances within a modality for biometric verification performance improvement has received considerable attention. In this letter, we present an iris recognition method based on multi-instance fusion, which combines the left and right irises of an individual at the matching score level. When fusing, a novel fusion strategy using minimax probability machine (MPM) is applied to generate a fused score for the final decision. The experimental results on CASIA and UBIRIS databases show that the proposed method can bring obvious performance improvement compared with the single-instance method. The comparison among different fusion strategies demonstrates the superiority of the fusion strategy based on MPM.

Xianghua Yao

Papers

Iris recognition based on multialgorithmic fusion

Improving iris recognition performance via multi-instance fusion at the score level

Minimax Probability Machine Multialgorithmic Fusion for Iris Recognition