Towards an efficient Iris Recognition System on Embedded Devices

TL;DR

This paper presents a lightweight iris recognition system optimized for embedded devices, including a novel segmentation method, to enable efficient contactless biometric identification with limited hardware resources.

Contribution

It introduces a new lightweight iris segmentation model and evaluates its performance on embedded hardware for contactless iris recognition.

Findings

The Unet_xxs segmentation model achieves high accuracy with low memory usage.

Embedded systems can perform iris recognition efficiently with the proposed setup.

The system demonstrates promising speed-performance trade-offs on different hardware.

Abstract

Iris Recognition (IR) is one of the market's most reliable and accurate biometric systems. Today, it is challenging to build NIR-capturing devices under the premise of hardware price reduction. Commercial NIR sensors are protected from modification. The process of building a new device is not trivial because it is required to start from scratch with the process of capturing images with quality, calibrating operational distances, and building lightweight software such as eyes/iris detectors and segmentation sub-systems. In light of such challenges, this work aims to develop and implement iris recognition software in an embedding system and calibrate NIR in a contactless binocular setup. We evaluate and contrast speed versus performance obtained with two embedded computers and infrared cameras. Further, a lightweight segmenter sub-system called "Unet_xxs" is proposed, which can be used for iris semantic segmentation under restricted memory resources.