ORCNet: A Context-Based Network to Simultaneously Segment the Ocular Region Components

Accurate extraction of the Region of Interest is critical for successful ocular region-based biometrics. In this direction, we propose a new context-based segmentation approach, entitled Ocular Region Context Network (ORCNet), introducing a specific loss function, i.e., the Punish Context Loss (PC-Loss). The PC-Loss punishes the segmentation losses of a network by using a percentage difference value between the ground truth and the segmented masks. We obtain the percentage difference by taking into account Biederman’s semantic relationship concepts, in which we use three contexts (semantic, spatial, and scale) to evaluate the relationships of the objects in an image. Our proposal achieved promising results in the evaluated scenarios—iris, sclera, and ALL (iris + sclera) segmentations—, outperforming the literature baseline techniques. The ORCNet with ResNet-152 outperforms the best baseline (EncNet with ResNet-152) on average by 2. $$27\%$$ , 28. $$26\%$$ and 6. $$43\%$$ in terms of F-Score, Error Rate and Intersection Over Union, respectively. We also provide (for research purposes) 3191 manually labeled masks for the MICHE-I database, as another contribution of our work.