Journal Article

The analysis of Iris image acquisition and real-time detection system using convolutional neural network

Liu, Yanru, Yee, Austin Lin, Xu, Jiali

2024

Overview

The purpose is to explore the effect of iris image acquisition and real-time detection systems based on Convolutional Neural Network (CNN) and improve the efficiency of iris real-time detection. Based on existing iris data acquisition and detection systems, this study uses the light field focusing algorithm to collect iris data in live, introduces CNN in Deep Learning (DL) algorithm, and designs an iris image acquisition and live detection system based on CNN. Afterward, Radial Basis Function (RBF)-Support Vector Machines (SVM) algorithm is used to classify iris feature information. Based on Field Programmable Gate Array (FPGA), a system for iris image acquisition, processing, and display is constructed. Finally, the performance of the constructed system and algorithm are analyzed through simulation experiments. The research results show that the proposed algorithm can automatically select the qualified iris images in live, significantly improve the recognition accuracy of the whole iris recognition system, and the average time of live quality evaluation for each frame image is less than 0.05 s. The focal point of the investigation involves the exploration of a CNN-based iris image acquisition and real-time detection system, with an emphasis on enhancing the efficiency of real-time iris detection. The innovation of this research lies in the integration of deep learning algorithms and light-field focusing techniques, applied to the reconstruction of a FPGA system. Further, the proposed algorithm is compared with Super-Resolution Using Very Deep Convolutional Networks (VDSR), Deeply Recursive Convolutional Network (DRCN), Residual Dense Network (RDN), and Bicubic. The comparison analysis suggests that the recognition accuracy of the proposed algorithm is the highest, close to 100%. Additionally, the proposed algorithm is compared with the Image Quality Evaluation-based Algorithm (IQA) and the Feature Extraction-based Algorithm (FEA), showing that the proposed RBF-SVM algorithm has higher classification accuracy (96.38%) and lower Average Classification Error Rate (ACER) (3.69%). The research results can provide a reference for live iris image detection and data acquisition.

• Publisher: Springer US,Springer Nature B.V
• Subject: Accuracy / Algorithms / Artificial neural networks / Batch processing / Biometric identification / Biometric recognition systems / Biometrics / Cameras / Classification / Compilers / Computer Science / Data acquisition / Deep learning / Feature extraction / Field programmable gate arrays / Focusing / Fourier transforms / Image acquisition / Image detection / Image quality / Image reconstruction / Interpreters / Iris / Machine learning / Neural networks / Processor Architectures / Programming Languages / Quality assessment / Radial basis function / Real time / Research methodology / Support vector machines / Usability