Determining Intoxication With Machine Learning Analysis of Eyes

Researchers from Germany and Chile have developed a new machine learning framework capable of evaluating whether a person is intoxicated, based on near infra-red images of their eyes. The research is aimed at the development of'fitness for duty' real-time systems capable of assessing the readiness of an individual to perform critical tasks such as driving, or operating machinery, and uses a novel and scratch-trained object detector that can individuate a subject's eye components from a single image and evaluate them against a database that includes intoxicated and non-intoxicated eye images. You Only Look Once (YOLO) individuates the subject's eyes, after which the framework separates the instances and performs segmentation to break the eye image down into its constituent parts. Initially the system captures and individuates an image of each eye with the You-Only-Look-Once (YOLO) object detection framework. After this, two optimized networks are used to break down the eye images into semantic regions – the Criss Cross attention network (CCNet) released in 2020 by the Huazhong University of Science and Technology, and the DenseNet10 segmentation algorithm, also developed by several of the new paper's researchers at Chile.