# Constrained Design of Deep Iris Networks

## TL;DR

This paper introduces a constrained optimization approach to designing deep iris networks, achieving state-of-the-art performance with reduced model size and computational cost, while also analyzing the optimality of classic IrisCode methods.

## Contribution

It proposes a novel constrained optimization framework for automated iris network design that balances accuracy, model compactness, and computational efficiency.

## Key findings

- Achieves state-of-the-art iris recognition accuracy.
- Reduces model size and FLOPs compared to existing networks.
- Provides insights into the optimality of classic IrisCode methods.

## Abstract

Despite the promise of recent deep neural networks in the iris recognition setting, there are vital properties of the classic IrisCode which are almost unable to be achieved with current deep iris networks: the compactness of model and the small number of computing operations (FLOPs). This paper re-models the iris network design process as a constrained optimization problem which takes model size and computation into account as learning criteria. On one hand, this allows us to fully automate the network design process to search for the best iris network confined to the computation and model compactness constraints. On the other hand, it allows us to investigate the optimality of the classic IrisCode and recent iris networks. It also allows us to learn an optimal iris network and demonstrate state-of-the-art performance with less computation and memory requirements.