An Unsupervised Approach for Eye Sclera Segmentation

We present an unsupervised sclera segmentation method foreye color images. The proposed approach operates on a visible spectrumRGB eye image and does not require any prior knowledge such as eyelidor iris center coordinate detection. The eye color input image is enhancedby an adaptive histogram normalization to produce a gray level image inwhich the sclera is highlighted. A feature extraction process is involvedboth in the image binarization and in the computation of scores to assignto each connected components of the foreground. The binarization processis based on clustering and adaptive thresholding. Finally, the selectionof foreground components identifying the sclera is performed on theanalysis of the computed scores and of the positions between the foregroundcomponents. The proposed method was ranked 2nd in the ScleraSegmentation and Eye Recognition Benchmarking Competition (SSRBC2017), providing satisfactory performance in terms of precision.