  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.
