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Severalsystems require authenticating a person's identity before giving access to resources. With new advances in technology, biometrics is one of the most promising techniques in human recognition. Biometrics intends to identify a person by his physical and/or behavioural characteristics. This paper presents an approach for human authentication based on iris texture analysis. In this paper, we present a HW/SW implementation algorithm for detection and localisation of iris based on shape properties. The designed system has been implemented in a CYCLONE II DE2 Board using the NIOS II processor which is characterized by its flexibility and programmability. The data base CASIA version 1.0 is used for the test. The experimental results of the approach show the performance of our algorithms.

This paper presents a hardware implementation of morphological operations based on dynamic and partial reconfiguration (DPR) technique. This technique allows reconfiguring a part of the FPGA area with different functionalities at runtime. It is a promising solution toincrease performance in the system. Our design allows todesigner to choose the adequate morphological operation (erosion or dilation) according the image constraints. We use xilinx tools and Virtex-5 FPGA board. To evaluate our implementation, we measure the performance in terms of area occupation and time reconfiguration. The results show the implementation of morphological operations on FPGA (Field Programmable Gate Array) using DPR can improve the performance and saving at least 11 % of area

International audienceIn this paper, we present a software implementation of an iris recognition algorithm on a FPGA (Field Programmable Gate Array). We use the OSIRIS reference systems developed in the framework of the BioSecure project which is an implementation of the well-knoun Daugman approach [10]. This system is composed of two modules. The first one uses the Hough transform to segment and normalize the iris. The second module applies a Gabor filter on the normalized image and calculates the similarity score between two images using the Hamming distance. OSIRIS algorithm has performance (EER=7.04%) when it is running on the PC in ICE 2005 Database. This system is coded in C++ using the library OpenCV (Open Source Computer Vision). This library is difficult to implement it in FPGA. that's why, we first remove this library in our code than improve this algorithm. Finally, we implement it on a CYCLONE II DE2 Board FPGA using the NIOS II processor which is characterized by its flexibility and programmability

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