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