2021/07/08 by Longyu Ma, Chiu-Wing Sham, Ma, Longyu +6
Biochemistry, Genetics and Molecular Biology · Computer Science · #Biometric Identification and Security #Cellular Automata and Applications #Computer Vision and Pattern Recognition (cs.CV) #DNA and Biological Computing #FOS: Computer and information sciences #cs.CV
paper · pdf · doi:10.48550/arxiv.2107.03688
8 pages, 6 figures
arxiv created 2021/07/08 · openalex publication_date 2021/07/08 · arxiv updated 2021/07/09 · openalex created_date 2021/07/19 · openalex updated_date 2026/07/28
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.