Iris recognition is one of the most powerful techniques for biometric identification ever developed. Commercial systems based on the algorithms developed by John Daugman have been available since 1995 and have been used in a variety of practical applications. However, all currently available systems impose substantial constraints on subject position and motion during the recognition process. These constraints are largely driven by the image acquisition process, rather than the particular pattern-matching algorithm used for the recognition process. In this paper we present results of our efforts to substantially reduce constraints on position and motion by means of a new image acquisition system based on high-resolution cameras, video synchronized strobed illumination, and specularity based image segmentation. We discuss the design tradeoffs we made in developing the system and the performance we have been able to achieve when the image acquisition system is combined with a standard iris recognition algorithm. The Iris on the Move (IOM) system is the first system to enable capture of iris images of sufficient quality for iris recognition while the subject is moving at a normal walking pace through a minimally confining portal

The purpose of this study is to develop an adaptive model of a very large scale data processing and storage environment. The target environment includes grid applications such as health-care and finance in which the data may be located primarily within the resources of a worldwide corporation. The approach is to use phase identification techniques that can detect over-utilized grid resources, and then to make dynamic decisions to reassign additional resources to that portion of the application processing. Two phase identification techniques are proposed, a variation technique and a real-time threshold-based technique. The techniques are validated with a simulation model and a case study using measured data from a production grid environment. The case study demonstrates that phase identification techniques can be used as the intelligent component of a reactive mechanism for a grid to adapt to changing environmental conditions by dynamic automatic reconfiguration. Results show that threshold based phase identifying techniques combined with dynamic resource allocation capabilities are effective in alleviating performance hot spots and improving response time in a large scale data grid.