Abstract not found

A biometric system provides automatic identification of an individual based on a unique feature or characteristic possessed by the individual. Iris recognition is regarded as the most reliable and accurate biometric identification system available. Most commercial iris recognition systems use patented algorithms developed by Daugman, and these algorithms are able to produce perfect recognition rates. However, published results have usually been produced under favourable conditions, and there have been no independent trials of the technology. The work presented in this thesis involved developing an ‘open-source ’ iris recognition system in order to verify both the uniqueness of the human iris and also its performance as a biometric. For determining the recognition performance of the system two databases of digitised greyscale eye images were used. The iris recognition system consists of an automatic segmentation system that is based on the Hough transform, and is able to localise the circular iris and pupil region, occluding eyelids and eyelashes, and reflections. The extracted iris region was then

Abstract—Iris biometric systems apply filters to iris images to extract information about iris texture. Daugman’s approach maps the filter output to a binary iris code. The fractional Hamming distance between two iris codes is computed and decisions about the identity of a person are based on the computed distance. The fractional Hamming distance weights all bits in an iris code equally. However, not all of the bits in an iris code are equally useful. Our research is the first to present experiments documenting that some bits are more consistent than others. Different regions of the iris are compared to evaluate their relative consistency and, contrary to some previous research, we find that the middle bands of the iris are more consistent than the inner bands. The inconsistent-bit phenomenon is evident across genders and different filter types. Possible causes of inconsistencies, such as segmentation, alignment issues, and different filters, are investigated. The inconsistencies are largely due to the coarse quantization of the phase response. Masking iris code bits corresponding to complex filter responses near the axes of the complex plane improves the separation between the match and nonmatch Hamming distance distributions. Index Terms—Iris biometrics, iris code, texture filter, false reject rate. Ç 1

Biometric authentication of gait, anthropometric data, human activities and movement disorders are presented in this paper using the Continuous Human Movement Recognition (CHMR) framework introduced in Part I. A novel biometric authentication of anthropometric data is presented based on the realization that no one is average sized in as many as 10 dimensions. These body part dimensions are quantified using the CHMR body model. Gait signatures are then evaluated using motion vectors, temporally segmented by gait dynemes, and projected into a gait space for an eigengait based biometric authentication. Left-right asymmetry of gait is also evaluated using robust CHMR left-right labeling of gait strides. Accuracy of the gait signature is further enhanced by incorporating the knee-hip angle-angle relationship popular in biomechanics gait research, together with other gait parameters. These gait and anthropometric biometrics are fused to further improve accuracy. The next biometric identifies human activities which requires a robust segmentation of the many skills encompassed. For this reason, the CHMR activity model is used to identify various activities from making a coffee to using a computer. Finally, human movement disorders were evaluated by studying patients with dopa-responsive Parkinsonism and age matched normals who were video taped during several gait cycles to determine a robust metric for classifying movement disorders. The results suggest that the R. D. Green is with the Human Interface Technology Lab, University of Canterbury, Christchurch, New Zealand. He was with the School of Electrical and Information Engineering, The University of Sydney, NSW 2006, Australia, (e-mail: richard.green@canterbury.ac.nz).

Effect of severe image compression on iris recognition performance

Image quality assessment plays an important role in automated biometric systems for two reasons: (i) system performance and (ii) interoperability. In this paper we assess image quality from the iris biometric. We study the impact of various factors on performance as well as evaluate which factors can be feasibly compensated. There has been no extensive study on iris image quality from a system performance perspective, though such studies exist for biometrics

Abstract — Today, campus grids provide users with easy access to thousands of CPUs. However, it is not always easy for nonexpert users to harness these systems effectively. A large workload composed in what seems to be the obvious way by a naive user may accidentally abuse shared resources and achieve very poor performance. To address this problem, we argue that campus grids should provide end users with high-level abstractions that allow for the easy expression and efficient execution of data intensive workloads. We present one example of an abstraction – All-Pairs – that fits the needs of several applications in biometrics, bioinformatics, and data mining. We demonstrate that an optimized All-Pairs abstraction is both easier to use than the underlying system, achieves performance orders of magnitude better than the obvious but naive approach, and is both faster and more efficient than a tuned conventional approach. This abstraction has been in production use for one year on a 500-CPU campus grid at the University of Notre Dame, and has been used to carry out a groundbreaking analysis of biometric data.

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Abstract: Biometric recognition refers to an automatic recognition of individuals based on a feature vector(s) derived from their physiological and/or behavioral characteristic. Biometric recognition systems should provide a reliable personal recognition schemes to either confirm or determine the identity of an individual. Applications of such a system include computer systems security, secure electronic banking, mobile phones, credit cards, secure access to buildings, health and social services. By using biometrics a person could be identified based on "who she/he is " rather then "what she/he has " (card, token, key) or "what she/he knows" (password, PIN). In this paper, a brief overview of biometric methods, both unimodal and multimodal, and their advantages and disadvantages, will be presented.

Blend of cryptography and biometrics results an emerging architecture known as Crypto-biometrics which produces high level security. Fuzzy vault is a cryptographic construction used to store iris biometric templates which are binded by a random key extracted from same iris textures. Though the fuzzy vault provides better security, it is affected by cross matching, non uniform nature of biometric data. To overcome these limitations, we propose a scheme that hardens both fuzzy vault and secret key using password. By using password an additional layer of security is embedded to achieve high level security. Key words: Crypto-biometric, fuzzy vault, hardening, morphological operations, chaff point. 1.