A novel method for iris recognition using BP neural network and parallel computing by the aid of GPUs (Graphics Processing Units)
This work addresses the need for faster and more efficient iris recognition systems, though it appears incremental as it builds on existing methods like BPNN and GPU acceleration.
The paper tackled iris recognition by combining Haar wavelet feature extraction with a back propagation neural network classifier, achieving high-speed processing through parallel computing on GPUs using CUDA.
In this paper, we seek a new method in designing an iris recognition system. In this method, first the Haar wavelet features are extracted from iris images. The advantage of using these features is the high-speed extraction, as well as being unique to each iris. Then the back propagation neural network (BPNN) is used as a classifier. In this system, the BPNN parallel algorithms and their implementation on GPUs have been used by the aid of CUDA in order to speed up the learning process. Finally, the system performance and the speeding outcomes in a way that this algorithm is done in series are presented.