VHDL modeling of IRIS recognition using neural network - Reaz, Mamun Bin Ibne; T., A. Leng; Mohd-Yasin, Faisal; Sulaiman, M. S.; Islam, S.; Ibrahimy, Muhammad Ibn (2004)
The application of neural networks technology to real-time processing of biometric identification demands the development of a new processing structure that allows efficient hardware implementation of the neural networks mechanism. This paper describes a VHDL modeling environment of IRIS recognition for biometric identification using neural network to ease the description, verification, simulation and hardware realization of this kind of systems. Iris has unique features to be used as a biometric signature due to its speed, simplicity, accuracy, and applicability. The processes of the project consist of two main parts, which are image processing and recognition. Image processing done by using Matlab where back propagation was used for recognition. The iris recognition neural network architecture comprises three layers: input layer with three neurons, hidden layer with two neurons and output layer with one neuron. Sigmoid transfer function is used for both hidden layer and output layer neurons. Neuron of each layer is modeled individually using behavioral VHDL. The layers are then connected using structural VHDL. This is followed by the timing analysis for the validation, functionality and performance of the designated model. Iris vector from captured human iris has been used to validate the effectiveness of the model. Test on the sample of 100 data showed an accuracy of 88.6% in recognizing the sample of irises.