An Embedded Iris Recognition System Optimization using Dynamically ReconfigurableDecoder with LDPC Codes
Abstract
Extracting and analyzing iris textures for biometric recognition has been extensively studied. As the transition of iris recognition from lab technology to nation-scale applications, most systems are facing high complexity in either time or space, leading to unfitness for embedded devices. In this paper, the proposed design includes a minimal set of computer vision modules and multi-mode QC-LDPC decoder which can alleviate variability and noise caused by iris acquisition and follow-up process. Several classes of QC-LDPC code from IEEE 802.16 are tested for the validity of accuracy improvement. Some of the codes mentioned above are used for further QC-LDPC decoder quantization, validation and comparison to each other. We show that we can apply Dynamic Partial Reconfiguration technology to implement the multi-mode QC-LDPC decoder for the iris recognition system. The results show that the implementation is power-efficient and good for edge applications.
Cite
@article{arxiv.2107.03688,
title = {An Embedded Iris Recognition System Optimization using Dynamically ReconfigurableDecoder with LDPC Codes},
author = {Longyu Ma and Chiu-Wing Sham and Chun Yan Lo and Xinchao Zhong},
journal= {arXiv preprint arXiv:2107.03688},
year = {2021}
}
Comments
8 pages, 6 figures