Eye Localisation using Cascaded U-Net for Autism Spectrum Disorder

Many computer vision applications Computer Aided Diagnosis require an accurate and efficient eye detector. We represent, in this work, an efficient approach for determining the position of the eye in images presenting faces. First, a series of candidate regions are proposed; next, a set of cascaded U-net is used to detect the eye regions; then edge detection methods are used to detect eyes lids and iris boundary and thus helping determining the gaze direction of person having ASD. Our proposed approach achieved a precision of detection that is better than the current most recent methods in trials utilizing GI4E, BioID, and columbiaGaze datasets. The proposed approach is robust to picture alterations, such as changes in external illumination, facial occlusion, and image modality.