Sclera-Net: Accurate Sclera Segmentation in Various Sensor Images Based on Residual Encoder and Decoder Network

Abstract

Sclera segmentation is revealed to be of noteworthy importance for ocular biometrics. The paramount step for biometric recognition methods is the segmentation of the area of interest, i.e., the sclera in our case. The sclera segmentation process plays a pivotal part in retaining the accuracy of the sclera-based recognition schemes by restraining the errors. However, accurate sclera segmentation in the images from various sensors in a real environment is quite challenging due to the saturated and/or defocused vessel patterns and the vessel structure, which has complex nonlinear deformations due to the multilayered sclera. With the development of deep learning algorithms, studies that are based on the sclera segmentation using convolutional neural networks (CNNs) have achieved promising results for sclera recognition. However, previous CNN-based methods are based on the repeated subsampling stages of convolution strides, or spatial pooling leads to losing much of the finer image structure that significantly decreases overall performance in tasks, such as semantic segmentation. In this paper, we present Sclera-Net, a residual encoder and decoder network that exploits identity and non-identity mapping residual skip connections to take benefit of the high-frequency information from the prior layers of both encoder and decoder networks to determine the accurate sclera region as well as other ocular regions. In this way, the finer image structure that was being lost due to repeated subsampling during convolution and pooling can be reutilized using residual skip connections to enhance overall performance. Furthermore, the proposed Sclera-Net does not enhance the performance on the cost of increasing depth, complexity, or the number of parameters. We performed comprehensive experiments and obtained optimum performance not only on sclera datasets but also on the iris datasets. In particular, we achieved an equal error rate and mean F1-score of 0.0093 and 96.2421, respectively, on the challenging SBVPI database, which is the best-reported result to date.