  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.
