Towards an efficient Iris Recognition System on Embedded Devices
This work addresses the problem of creating cost-effective and efficient iris recognition systems for embedded devices, which is incremental as it builds on existing methods with optimizations for hardware constraints.
The paper tackled the challenge of building an efficient iris recognition system on embedded devices by developing and implementing software, calibrating a contactless binocular NIR setup, and proposing a lightweight segmentation sub-system called 'Unet_xxs' for iris semantic segmentation under restricted memory resources, evaluating speed versus performance on two embedded computers and infrared cameras.
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.