A novel method for iris recognition using BP neural network and parallel computing by the aid of GPUs (Graphics Processing Units)

TL;DR

This paper introduces a fast iris recognition method combining Haar wavelet features, BP neural networks, and GPU parallel computing to enhance speed and accuracy.

Contribution

It presents a novel iris recognition approach that leverages GPU-accelerated BPNN training with Haar wavelet features for improved performance.

Findings

High-speed feature extraction using Haar wavelets

GPU-based parallel BPNN training accelerates recognition

System achieves faster processing compared to serial algorithms

Abstract

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.