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This paper presents a new technique for solving the unsupervised clustering problem using a pulse based RBF neural network that is optimized by a genetic algorithm. In this new approach, the neuron encodes the information using the firing times of pulses that are generated by the neurons. Due to hardware's speed advantage and its ability to parallelize along with reprogramability and reconfigurability, the field programmable gate array (FPGA) is used for implementing the proposed approach on-chip. The developed on-chip system is capable of solving various problems requiring large data set in real time. Experimental verification using a sample data set and Fisher's Iris data set has shown the feasibility of the developed system. [ABSTRACT FROM AUTHOR]

In this paper, an identity verification framework which combines pattern information from the palm-vein and the palm-crease texture is proposed. Main feature of this system is the use of a low cost Near-Infra-Red (NIR) camera instead of the more expensive infra-red thermal camera for palm image capture. Our preliminary experiments show that useful information from palm-vein and palm-crease texture can be effectively extracted for identity verification using a simple setup to contain the camera. Keywords:Biometrics, Multimodal Biometrics, Palm-vein Recognition, Palm-print Recognition and Pattern Classification. [ABSTRACT FROM AUTHOR]