  Applications of Fully Convolutional Networks (FCN) in iris segmentation have
shown promising advances. For mobile and embedded systems, a significant
challenge is that the proposed FCN architectures are extremely computationally
demanding. In this article, we propose a resource-efficient, end-to-end iris
recognition flow, which consists of FCN-based segmentation, contour fitting,
followed by Daugman normalization and encoding. To attain accurate and
efficient FCN models, we propose a three-step SW/HW co-design methodology
consisting of FCN architectural exploration, precision quantization, and
hardware acceleration. In our exploration, we propose multiple FCN models, and
in comparison to previous works, our best-performing model requires 50X less
FLOPs per inference while achieving a new state-of-the-art segmentation
accuracy. Next, we select the most efficient set of models and further reduce
their computational complexity through weights and activations quantization
using 8-bit dynamic fixed-point (DFP) format. Each model is then incorporated
into an end-to-end flow for true recognition performance evaluation. A few of
our end-to-end pipelines outperform the previous state-of-the-art on two
datasets evaluated. Finally, we propose a novel DFP accelerator and fully
demonstrate the SW/HW co-design realization of our flow on an embedded FPGA
platform. In comparison with the embedded CPU, our hardware acceleration
achieves up to 8.3X speedup for the overall pipeline while using less than 15%
of the available FPGA resources. We also provide comparisons between the FPGA
system and an embedded GPU showing different benefits and drawbacks for the two
platforms.
