SIFT Features and SVM Learning based Sclera Recognition Method with Efficient Sclera Segmentation for Identity Identification

In this work, based on local features of sclera veins, a learning based sclera recognition design is proposed for identity identification. The proposed system is partitioned into two-stage computations. The first stage is the preprocessing process, which includes pupil location, iris segmentation, sclera segmentation, and sclera vein enhancement. At the second stage, by the scale-invariant feature transform (SIFT) technology, the sclera vein features are extracted after image enhancements. By the K-means scheme, the proposed design merges the similar features together to construct a dictionary to describe the interested group features. Next, the sclera images refers the dictionary to get the histogram of group features, and the group features are fed into the support vector machine (SVM) to train an identity classifier. Finally, the sclera recognition tests are evaluated. By the UBIRISv1 dataset, the experimental results show that the recognition accuracy is up to near 100%.