Semantic Segmentation of Periocular Near-Infra-Red Eye Images Under Alcohol Effects

This paper proposes a new framework to detect, segment, and estimate the localization of eyes from periocular Near-Infra-Red iris image under alcohol consumption. stage will take part in final solution measure fitness for duty. Fitness systems allow us determine whether person is physically or psychologically able perform their tasks. Our segmentation based on an object detector trained scratch detect both single image. Then, two efficient networks were used semantic segmentation; Criss-Cross attention network DenseNet10, with only 122,514 210,732 parameters, respectively. These can find pupil, iris, sclera. In end, binary output eye mask pupil diameter estimation high precision. Five state-of-the-art algorithms this purpose. A mixed proposal reached best results. second contribution establishing behavior curve presence utilizing stream images captured instance. Also, manually labeled database more than 20k was created. method obtains mean Intersection-over-Union 94.54% DenseNet10 parameters error 1-pixel average.