Iris Codes Classification Using Discriminant and Witness Directions

The main topic discussed in this paper is how to use intelligence for biometric decision defuzzification. A neural training model is proposed and tested here as a possible solution for dealing with natural fuzzification that appears between the intra- and inter-class distribution of scores computed during iris recognition tests. It is shown here that the use of proposed neural network support leads to an improvement in the artificial perception of the separation between the intra- and inter-class score distributions by moving them away from each other.Comment: 6 pages, 5 figures, Proc. 5th IEEE Int. Symp. on Computational Intelligence and Intelligent Informatics (Floriana, Malta, September 15-17), ISBN: 978-1-4577-1861-8 (electronic), 978-1-4577-1860-1 (print

Predictive models for multibiometric systems

Recognizing a subject given a set of biometrics is a fundamental pattern recognition problem. This paper builds novel statistical models for multibiometric systems using geometric and multinomial distributions. These models are generic as they are only based on the similarity scores produced by a recognition system. They predict the bounds on the range of indices within which a test subject is likely to be present in a sorted set of similarity scores. These bounds are then used in the multibiometric recognition system to predict a smaller subset of subjects from the database as probable candidates for a given test subject. Experimental results show that the proposed models enhance the recognition rate beyond the underlying matching algorithms for multiple face views, fingerprints, palm prints, irises and their combinations

THE DANGERS OF FIGHTING TERRORISM WITH TECHNOCOMMUNITARIANISM: CONSTITUTIONAL PROTECTIONS OF FREE EXPRESSION, EXPLORATION, AND UNMONITORED ACTIVITY IN URBAN SPACES

Part I of this article examines how some commentators can plausibly argue that constitutional liberty and privacy protections do not protect the individual liberty and privacy that modern individuals have come to expect in many public spaces, particularly in urban environments. Constitutional liberalism, this section points out, makes this question a difficult one, because it is marked by scrupulous neutrality towards different visions of “the good life.” In other words, the constitutional order does not condemn those who choose a communitarian way of life and favor those who prefer individualism. Rather, it tolerates both of these (and other) preferences about one’s social and cultural environment, and leaves citizens free to opt for the life of their choice. Part II suggests that it is difficult to make sense of our modern jurisprudence of First Amendment rights, especially as they relate to anonymous communication and association on the Internet and elsewhere, unless one allows room in our constitutional law for a jurisprudence that “captures” and preserves social incarnations of liberty and privacy that were not yet in existence when theConstitution was drafted. Therefore, it is possible for for courts and others to find that freedom-enabling institutions that did not exist earlier in American history, and might cease to exist in the future, deserve certain constitutional protection while they are here. Part III explains that like the virtual liberation offered by the Internet, city life offered and continues to offer an invaluable refuge for substantial expressive activity and intellectual exploration that would be far more elusive without this type of urban existence. It provides individuals with an incredibly rich bazaar of ideas, and allows them to browse among these deas, substantially free from outside monitoring or control. While First Amendment law does not single out urban environments for protection, it protects such environments indirectly by preserving certain opportunities that are characteristic of modern urban life: opportunities for giving speeches to large crowds, for confronting strangers with ideas they may find unfamiliar or provocative, or for speaking or gathering information in the anonymity of the crowd

Ethnicity and Biometric Uniqueness: Iris Pattern Individuality in a West African Database

We conducted more than 1.3 million comparisons of iris patterns encoded from images collected at two Nigerian universities, which constitute the newly available African Human Iris (AFHIRIS) database. The purpose was to discover whether ethnic differences in iris structure and appearance such as the textural feature size, as contrasted with an all-Chinese image database or an American database in which only 1.53% were of African-American heritage, made a material difference for iris discrimination. We measured a reduction in entropy for the AFHIRIS database due to the coarser iris features created by the thick anterior layer of melanocytes, and we found stochastic parameters that accurately model the relevant empirical distributions. Quantile-Quantile analysis revealed that a very small change in operational decision thresholds for the African database would compensate for the reduced entropy and generate the same performance in terms of resistance to False Matches. We conclude that despite demographic difference, individuality can be robustly discerned by comparison of iris patterns in this West African population.Comment: 8 pages, 8 Figure

An Efficient Vein Pattern-based Recognition System

This paper presents an efficient human recognition system based on vein pattern from the palma dorsa. A new absorption based technique has been proposed to collect good quality images with the help of a low cost camera and light source. The system automatically detects the region of interest from the image and does the necessary preprocessing to extract features. A Euclidean Distance based matching technique has been used for making the decision. It has been tested on a data set of 1750 image samples collected from 341 individuals. The accuracy of the verification system is found to be 99.26% with false rejection rate (FRR) of 0.03%.Comment: IEEE Publication format, International Journal of Computer Science and Information Security, IJCSIS, Vol. 8 No. 1, April 2010, USA. ISSN 1947 5500, http://sites.google.com/site/ijcsis

Method for estimating potential recognition capacity of texture-based biometrics

When adopting an image-based biometric system, an important factor for consideration is its potential recognition capacity, since it not only defines the potential number of individuals likely to be identifiable, but also serves as a useful figure-of-merit for performance. Based on block transform coding commonly used for image compression, this study presents a method to enable coarse estimation of potential recognition capacity for texture-based biometrics. Essentially, each image block is treated as a constituent biometric component, and image texture contained in each block is binary coded to represent the corresponding texture class. The statistical variability among the binary values assigned to corresponding blocks is then exploited for estimation of potential recognition capacity. In particular, methodologies are proposed to determine appropriate image partition based on separation between texture classes and informativeness of an image block based on statistical randomness. By applying the proposed method to a commercial fingerprint system and a bespoke hand vein system, the potential recognition capacity is estimated to around 10^36 for a fingerprint area of 25  mm^2 which is in good agreement with the estimates reported, and around 10^15 for a hand vein area of 2268  mm^2 which has not been reported before