Entropy reduction and decorrelation in visual coding by orientedneural receptive fields

Image coding is optimally efficient if the coefficients of the representation are nonredundant, in the Shannon sense that their mutual information is zero. In that case, the code coefficients are uncorrelated and they form a statistically independent ensemble, so that the conditional probability of one coefficient's value, given any other coefficient's value, is the same as its unconditional probability: P(x/y) = P(x). In order for each coefficient to capture a unique property of the image that cannot be captured by any other coefficient, the expansion functions employed in the code must be linearly independent. In order for the code coefficients to have zero mutual information, the code primitives must be orthogonal so that their projections onto each other are always zero. In biological visual systems, although it is clear that some forms of efficiency (such as speed) are desirable, it is not obvious whether coding efficiency as measured by mutual information among the neurons is a factor which explains any of their properties. The center/surround receptive field profiles of neurons in the retina and geniculate are far from an orthogonal set, but a given neuron can still be regarded as a decorrelator of the incoming signal in the sense that it responds primarily to changes in the image (changes in space, time, chrominance, etc.) At the level of the brain's visual cortex, the introduction of the new variable of orientation selectivity can be regarded not only as a means for providing orientation labels for image structure, but also more basically as an effective decorrelator of the neural representation.(ABSTRACT TRUNCATED AT 250 WORDS)     

A Fast Search Algorithm for a Large Fuzzy Database

In this paper, we propose a fast search algorithm for a large fuzzy database that stores iris codes or data with a similar binary structure. The fuzzy nature of iris codes and their high dimensionality render many modern search algorithms, mainly relying on sorting and hashing, inadequate. The algorithm that is used in all current public deployments of iris recognition is based on a brute force exhaustive search through a database of iris codes, looking for a match that is close enough. Our new technique, Beacon Guided Search (BGS), tackles this problem by dispersing a multitude of ldquobeaconsrdquo in the search space. Despite random bit errors, iris codes from the same eye are more likely to collide with the same beacons than those from different eyes. By counting the number of collisions, BGS shrinks the search range dramatically with a negligible loss of precision. We evaluate this technique using 632,500 iris codes enrolled in the United Arab Emirates (UAE) border control system, showing a substantial improvement in search speed with a negligible loss of accuracy. In addition, we demonstrate that the empirical results match theoretical predictions.     

Probing the Uniqueness and Randomness of IrisCodes: Results From 200 Billion Iris Pair Comparisons

Recent large-scale deployments of iris recognition for border-crossing controls enable critical assessment of the robustness of this technology against making false matches, since vast numbers of cross comparisons become possible within large databases. This paper presents results from the 200 billion iris cross comparisons that could be performed within a database of 632 500 different iris images, spanning 152 nationalities. Each iris pattern was encoded into a phase sequence of 2048 bits using the Daugman algorithms. Empirically analyzing the tail of the resulting distribution of similarity scores enables specification of decision thresholds, and prediction of performance, of the iris recognition algorithms if deployed in identification mode on national scales     

Effect of Severe Image Compression on Iris Recognition Performance

We investigate three schemes for severe compression of iris images in order to assess what their impact would be on recognition performance of the algorithms deployed today for identifying people by this biometric feature. Currently, standard iris images are 600 times larger than the IrisCode templates computed from them for database storage and search; but it is administratively desired that iris data should be stored, transmitted, and embedded in media in the form of images rather than as templates computed with proprietary algorithms. To reconcile that goal with its implications for bandwidth and storage, we present schemes that combine region-of-interest isolation with JPEG and JPEG2000 compression at severe levels, and we test them using a publicly available database of iris images. We show that it is possible to compress iris images to as little as 2000 bytes with minimal impact on recognition performance. Only some 2% to 3% of the bits in the IrisCode templates are changed by such severe image compression, and we calculate the entropy per code bit introduced by each compression scheme. Error tradeoff curve metrics document very good recognition performance despite this reduction in data size by a net factor of 150, approaching a convergence of image data size and template size.     

Statistical Richness of Visual Phase Information: Update on Recognizing Persons by Iris Patterns

Algorithms first described in 1993 for recognizing persons by their iris patterns have now been tested in several public field trials, producing no false matches in several million comparison tests. The underlying recognition principle is the failure of a test of statistical independence on texture phase structure as encoded by multi-scale quadrature wavelets. The combinatorial complexity of this phase information across different persons spans about 244 degrees of freedom and generates a discrimination entropy of about 3.2 bits/mm² over the iris, enabling real-time decisions about personal identity with extremely high confidence. This paper reviews the current algorithms and presents the results of 2.3 million comparisons among eye images acquired in trials in Britain, the USA, and Japan, and it discusses aspects of the process still in need of improvement.     

New methods in iris recognition.

This paper presents the following four advances in iris recognition: 1) more disciplined methods for detecting and faithfully modeling the iris inner and outer boundaries with active contours, leading to more flexible embedded coordinate systems; 2) Fourier-based methods for solving problems in iris trigonometry and projective geometry, allowing off-axis gaze to be handled by detecting it and "rotating" the eye into orthographic perspective; 3) statistical inference methods for detecting and excluding eyelashes; and 4) exploration of score normalizations, depending on the amount of iris data that is available in images and the required scale of database search. Statistical results are presented based on 200 billion iris cross-comparisons that were generated from 632500 irises in the United Arab Emirates database to analyze the normalization issues raised in different regions of receiver operating characteristic curves.     

High confidence visual recognition of persons by a test ofstatistical independence

A method for rapid visual recognition of personal identity is described, based on the failure of a statistical test of independence. The most unique phenotypic feature visible in a person's face is the detailed texture of each eye's iris. The visible texture of a person's iris in a real-time video image is encoded into a compact sequence of multi-scale quadrature 2-D Gabor wavelet coefficients, whose most-significant bits comprise a 256-byte “iris code”. Statistical decision theory generates identification decisions from Exclusive-OR comparisons of complete iris codes at the rate of 4000 per second, including calculation of decision confidence levels. The distributions observed empirically in such comparisons imply a theoretical “cross-over” error rate of one in 131000 when a decision criterion is adopted that would equalize the false accept and false reject error rates. In the typical recognition case, given the mean observed degree of iris code agreement, the decision confidence levels correspond formally to a conditional false accept probability of one in about 10³¹     

Face and gesture recognition: overview

Computerised recognition of faces and facial expressions would be useful for human-computer interface, and provision for facial animation is to be included in the ISO standard MPEG-4 by 1999. This could also be used for face image compression. The technology could be used for personal identification, and would be proof against fraud. Degrees of difference between people are discussed, with particular regard to identical twins. A particularly good feature for personal identification is the texture of the iris. A problem is that there is more difference between images of the same face with, e.g., different expression or illumination, than there sometimes is between images of different faces. Face recognition by the brain is discussed