Ocular-Net: Lite-Residual Encoder Decoder Network for Accurate Ocular Regions Segmentation in Various Sensor Images

Ocular regions such as iris and sclera yield high accuracy in user's biometrics as well as liveness detection systems. The primary purpose for ocular recognition system is the accurate segmentation of the regions of interest that plays the key role in retaining the accuracy and restraining the errors in the whole system. However, accurate ocular regions segmentation in the images in a physical environment is very challenging owing to the images with low resolution, occlusion, blur, ghost effect, unusual glint, and off-angles. Deep learning algorithms with a convolutional neural network (CNN) has achieved promising results for ocular regions segmentation. However, previous CNNbased methods are unable to find the true boundary of ocular regions in non-ideal situations, which results in reduced reliability and accuracy. To overcome these challenges, we present Ocular-Net, a deep learning-based lite-residual encoder-decoder network to determine the accurate ocular regions such as iris and sclera. In this way, the true ocular regions can be segmented with the transfer of high-frequency information using residual skip connections. Additionally, the proposed Ocular-Net does not enhance performance on the cost of increasing depth, complexity or number of parameters, in fact, it has much fewer parameters than the previous state-of-the-art methods. We performed comprehensive experiments and obtained optimum performance on iris and sclera datasets.