Iris Recognition (IR) is one of the market's most reliable and accurate biometric systems. Today, it is challenging to build NearInfraRed (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. The evaluations reveal that Unet_xxs reduces the number of parameters by 77% and duplicates the speed of state-of-the-art segmentation models with an EER drop smaller than 1% in Iris Recognition with 8.06 frame per second (fps) and Intersection Over Union (IOU) of 0.8382.