Artificial Intelligence-based Semantic Segmentation of Ocular Regions for Biometrics and Healthcare Applications

Multiple ocular region segmentation plays an important role in different applications such as biometrics, liveness detection, healthcare, and gaze estimation. Typically, segmentation techniques focus on a single region of the eye at a time. Despite the number of obvious advantages, very limited research has focused on multiple regions of the eye. Similarly, accurate segmentation of multiple eye regions is necessary in challenging scenarios involving blur, ghost effects low resolution, off-angles, and unusual glints. Currently, the available segmentation methods cannot address these constraints. In this paper, to address the accurate segmentation of multiple eye regions in unconstrainted scenarios, a lightweight outer residual encoder-decoder network suitable for various sensor images is proposed. The proposed method can determine the true boundaries of the eye regions from inferior-quality images using the high-frequency information flow from the outer residual encoder-decoder deep convolutional neural network (called ORED-Net). Moreover, the proposed ORED-Net model does not improve the performance based on the complexity, number of parameters or network depth. The proposed network is considerably lighter than previous state-of-theart models. Comprehensive experiments were performed, and optimal performance was achieved using SBVPI and UBIRIS.v2 datasets containing images of the eye region. The simulation results obtained using the proposed OREDNet, with the mean intersection over union score (mIoU) of 89.25 and 85.12 on the challenging SBVPI and UBIRIS.v2 datasets, respectively.