Publications

@incollection{ScleraNetChapter,

title = {Deep Sclera Segmentation and Recognition},

author = {Peter Rot and Matej Vitek and Klemen Grm and Žiga Emeršič and Peter Peer

and Vitomir Štruc},

editor = {Andreas Uhl and Christoph Busch and Sebastien Marcel and Rainer Veldhuis},

url = {https://link.springer.com/content/pdf/10.1007%2F978-3-030-27731-4_13.pdf},

doi = {https://doi.org/10.1007/978-3-030-27731-4_13},

isbn = {978-3-030-27731-4},

year  = {2019},

date = {2019-11-14},

booktitle = {Handbook of Vascular Biometrics},

pages = {395-432},

publisher = {Springer},

chapter = {13},

series = {Advances in Computer Vision and Pattern Recognition},

abstract = {In this chapter, we address the problem of biometric identity recognition from the vasculature of the human sclera. Specifically, we focus on the challenging task of multi-view sclera recognition, where the visible part of the sclera vasculature changes from image to image due to varying gaze (or view) directions. We propose a complete solution for this task built around Convolutional Neural Networks (CNNs) and make several contributions that result in state-of-the-art recognition performance, i.e.: (i) we develop a cascaded CNN assembly that is able to robustly segment the sclera vasculature from the input images regardless of gaze direction, and (ii) we present ScleraNET, a CNN model trained in a multi-task manner (combining losses pertaining to identity and view-direction recognition) that allows for the extraction of discriminative vasculature descriptors that can be used for identity inference. To evaluate the proposed contributions, we also introduce a new dataset of ocular images, called the Sclera Blood Vessels, Periocular and Iris (SBVPI) dataset, which represents one of the few publicly available datasets suitable for research in multi-view sclera segmentation and recognition. The datasets come with a rich set of annotations, such as a per-pixel markup of various eye parts (including the sclera vasculature), identity, gaze-direction and gender labels. We conduct rigorous experiments on SBVPI with competing techniques from the literature and show that the combination of the proposed segmentation and descriptor-computation models results in highly competitive recognition performance.},

keywords = {biometrics, CNN, deep learning, ocular, sclera, segmentation, vasculature},

pubstate = {published},

tppubtype = {incollection}

}