Conditional random fields incorporate convolutional neural networks for human eye sclera semantic segmentation

Sclera segmentation as an ocular biometric has been of an interest in a variety of security and medical applications. The current approaches mostly rely on handcrafted features which make the generalisation of the learnt hypothesis challenging encountering images taken from various angles, and in different visible light spectrums. Convolutional Neural Networks (CNNs) are capable of extracting the corresponding features automatically. Despite the fact that CNNs showed a remarkable performance in a variety of image semantic segmentations, the output can be noisy and less accurate particularly in object boundaries. To address this issue, we have used Conditional Random Fields (CRFs) to regulate the CNN outputs. The results of applying this technique to sclera segmentation dataset (SSERBC 2017) are comparable with the state of the art solutions.