An Embedded Iris Recognition System Optimization using Dynamically ReconfigurableDecoder with LDPC Codes

TL;DR

This paper presents an optimized embedded iris recognition system utilizing a dynamically reconfigurable QC-LDPC decoder, improving accuracy and power efficiency for edge devices in large-scale biometric applications.

Contribution

It introduces a multi-mode QC-LDPC decoder with dynamic partial reconfiguration for iris recognition, reducing complexity and enhancing suitability for embedded and edge devices.

Findings

Power-efficient implementation achieved

Improved accuracy with selected QC-LDPC codes

Suitable for large-scale and real-time iris recognition

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.