SSERBC 2017: Sclera segmentation and eye recognition benchmarking competition

Abstract

This paper summarises the results of the Sclera Segmentation and Eye Recognition Benchmarking Competition (SSERBC 2017). It was organised in the context of the International Joint Conference on Biometrics (IJCB 2017). The aim of this competition was to record the recent developments in sclera segmentation and eye recognition in the visible spectrum (using iris, sclera and peri-ocular, and their fusion), and also to gain the attention of researchers on this subject. In this regard, we have used the Multi-Angle Sclera Dataset (MASD version 1). It is comprised of 2624 images taken from both the eyes of 82 identities. Therefore, it consists of images of 164 (82*2) eyes. A manual segmentation mask of these images was created to baseline both tasks. Precision and recall based statistical measures were employed to evaluate the effectiveness of the segmentation and the ranks of the segmentation task. Recognition accuracy measure has been employed to measure the recognition task. Manually segmented sclera, iris and periocular regions were used in the recognition task. Sixteen teams registered for the competition, and among them, six teams submitted their algorithms or systems for the segmentation task and two of them submitted their recognition algorithm or systems. The results produced by these algorithms or systems reflect current developments in the literature of sclera segmentation and eye recognition, employing cutting edge techniques. The MASD version 1 dataset with some of the ground truth will be freely available for research purposes. The success of the competition also demonstrates the recent interests of researchers from academia as well as industry on this subject.

Method for Reduction the Errors of Likelihood in the Authentication by the Iris

“…To assess changes in the quality of the image by using the biometric authentication system with iris functional visible and IR ranges scanning use Theorem multiplying the probabilities for independent events [6] according to formula (7): P (AB) = P (A) • P (B),…”

“…It should not be missed that in addition to lighting, you need to take into account the colour of the eyes of the person passing authentication. For example, the texture of the iris in people with dark eyes is poorly distinguishable in the visible range at registration, and vice versa, the texture of the iris in people with light eyes will be poorly distinguishable in the infrared range [6].…”

State of the Art in Vascular Biometrics

“…The authors are not aware of any open or public competition for retina biometrics. For sclera-based biometrics, sclera segmentation (and recognition) competitions have been organised 2015-2018 7 (SSBC'15 [45], SSRBC'16 [46], SSERBC'17 [48], SSBC'18 [47]) based on the SSRBC Dataset (2 eyes of 82 individuals, RGB, 4 angles) for which segmentation ground truth is being prepared. However, this dataset is not public and only training data are made available to participants of these competitions.…”

A Convolution Neural Network Engine for Sclera Recognition

“…A template based matching was introduced using hamming distance for classification in [23], [24]. For recognition, a benchmarking competition was organized to record the recent advancements in recognition techniques where the winning team achieved 72.56% accuracy in eye recognition [25]. From various literature, it is observed that many challenges are being addressed by various researchers to improve the accuracy of the recognition system.…”