Invited Talks
Zhenan Sun (China)
Iris recognition has many desirable properties for reliable individual authentication but usability is its largest bottleneck to wide deployment. Thus smart interface and machine intelligent are the objective of next-generation iris recognition. This paper presents the technology roadmap for smart iris recognition (SIR). Firstly, the concept of SIR is introduced, including its definition, characteristics and performance target. Then the evolution process of iris acquisition and recognition algorithm is proposed respectively. With various strategies of human-machine interaction, iris acquisition systems are grouped into seven categories, i.e. Close-range IR, Active IR, IR at a distance, Active IR at a distance, Passive IR on move, Active IR on move, IR for Surveillance. Iris recognition algorithms advance to be more accurate, robust, efficient and secure. The achievements of state-of-the-art iris recognition methods especially the contributions of our research group are reviewed in the roadmap.
Brian A. Barsky
Vision-simulated imaging (VSI) is the computer generation of synthetic images to simulate a subject's vision, by incorporating the characteristics of a particular individual's entire optical system. Using measured aberration data from a Shack-Hartmann wavefront aberrometry device, VSI modifies input images to simulate the appearance of the scene for the individual patient. Each input image can be a photograph, synthetic image created by computer, frame from a video, or standard Snellen acuity eye chart -- as long as there is accompanying depth information. An eye chart is very revealing, since it shows what the patient would see during an eye examination, and provides an accurate picture of his or her vision. Using wavefront aberration measurements, we determine a discrete blur function by sampling at a set of focusing distances, specified as a set of depth planes that discretize the three-dimensional space. For each depth plane, we construct an object-space blur filter. VSI methodology comprises several steps: (1) creation of a set of depth images, (2) computation of blur filters, (3) stratification of the image, (4) blurring of each depth image, and (5) composition of the blurred depth images to form a single vision-simulated image
Boris M. Velichkovsky
Recent progress in video-based eyetracking can be considered as a silent technological revolution in brain and behavioural sciences, approaching that of brain imaging methods. The importance of this methodology is quite obvious from the point of view of the ecological validity and practical implications of eyetracking. However, it is of a paramount significance for basic neurocognitive research as well. As a matter of fact, human eye movements are a common output of a number of philogenetically evolved and often (though not always) hierarchically organized brain systems. In this presentation, I will demonstrate how contemporary eyetracking research helps to disentangle their influences on task solution completing in a non-trivial way data obtained with neuroimaging methods.
Luc Florack, Evgeniya Balmashnova
We propose a tensorial expansion of high resolution diffusion imaging (HARDI) data on the unit sphere into a sum of self-similar polynomials, i.e. polynomials that retain their form up to a scaling under the act of lowering resolution via the diffusion semigroup generated by the Laplace-Beltrami operator on the sphere. In this way we arrive at a hierarchy of HARDI degrees of freedom into contravariant tensors of successive ranks, each characterized by a corresponding level of detail. We provide a closed-form expression for the scaling behaviour of each homogeneous term in the expansion, and show that classical diffusion tensor imaging (DTI) arises as an asymptotic state of almost vanishing resolution.
