When we expect important stimuli at a particular spatial location, how does our perceptual sensitivity change over space? Subjects were cued to expect a target stimulus at one location and then required to perform one of the following tasks at that and three other locations: luminance detection, brightness discrimination, orientation discrimination, or form discrimination. The analysis of subjects' performance according to signal detection theory revealed changes in both sensitivity and bias for each of these tasks. Sensitivity was maximally enhanced at the location where a target stimulus was expected and generally decreased with distance from that location. Factors that influenced the gradient of sensitivity were (a) the type of task performed and (b) the spatial distribution of the stimuli. Sensitivity fell off more steeply over distance for orientation and form discrimination than for luminance detection and brightness discrimination. In addition, it fell off more steeply when stimuli were near each other than when they were farther apart.

The mechanism by which visual-spatial attention affects the detection of faint signals has been the subject of considerable debate. It is well known that spatial cuing speeds signal detection. This may imply that attentional cuing modulates the processing of sensory information during detection or, alternatively, that cuing acts to create decision bias favoring input at the cued location. These possibilities were evaluated in 3 spatial cuing experiments. Peripheral cues were used in Experiment 1 and central cues were used in Experiments 2 and 3. Cuing similarly enhanced measured sensitivity, P(A) and d', for simple luminance detection in all 3 experiments. Under some conditions it also induced shifts in decision criteria (beta). These findings indicate that visual-spatial attention facilitates the processing of sensory input during detection either by increasing sensory gain for inputs at cued locations or by prioritizing the processing of cued inputs.

We investigated the randomness and uniqueness of human iris patterns by mathematically comparing 2.3 million di¡erent pairs of eye images. The phase structure of each iris pattern was extracted by demodulation with quadrature wavelets spanning several scales of analysis. The resulting distribution of phase sequence variation among di¡erent eyes was precisely binomial, revealing 244 independent degrees of freedom. This amount of statistical variability corresponds to an entropy (information density) of about 3.2 bits mm À2 over the iris. It implies that the probability of two di¡erent irides agreeing by chance in more than 70% of their phase sequence is about one in 7 billion. We also compared images of genetically identical irides, from the left and right eyes of 324 persons, and from monozygotic twins. Their relative phase sequence variation generated the same statistical distribution as did unrelated eyes. This indicates that apart from overall form and colour, iris patterns are determined epigenetically by random events in the morphogenesis of this tissue. The resulting diversity, and the combinatorial complexity created by so many dimensions of random variation, mean that the failure of a simple test of statistical independence performed on iris patterns can serve as a reliable rapid basis for automatic personal identi¢cation.

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 persons 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 government database of iris images. We show that it is possible to compress iris images to as little as 2 KB 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. Standard performance metrics such as error trade-off curves 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.

We argue that some aspects of human spatial vision, particularly for textured patterns and scenes, can be described in terms of demodulation and predictive coding. Such nonlinear processes encode a pattern into local phasors that represent it completely as a modulation, in phase and amplitude, of a prediction associated with the image structure in some region by its predominant undulation(s). The demodulation representation of a pattern is an anisotropic, second-order form of predictive coding, and it offers a particularly efficient way to analyze and encode textures, as it identifies and exploits their underlying redundancies. In addition, self-consistent domains of redundancy in image structure provide a basis for image segmentation. We first provide an algorithm for computing the three elements of a complete demodulation transform of any image, and we illustrate such decompositions for both natural and synthetic images. We then present psychophysical evidence from spatial masking experiments, as well as illustrations of perceptual organization, that suggest a possible role for such underlying representations in human vision. In psychophysical experiments employing masks with more than two oriented Fourier components, we find that peaks of threshold elevation occur at locations in the Fourier plane remote from the orientations and frequencies of the actual mask components. Rather, as would occur from demodulation, these peaks in the frequency plane are related to the vector difference frequencies between the actual masking components and their spectral centers of mass. We offer a neural interpretation of demodulation coding, and finally we demonstrate a practical application of this process in a system for automatic visual recognition of personal identity by demodulation of a facial feature.