An Online Learning-Based Adaptive Biometric System

2017 IEEE International Joint Conference on Biometrics (IJCB)

Ferrer

et al. 2017

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.

Section: Sclera Recognition Results and Discussionmentioning

confidence: 99%

SSERBC 2017: Sclera segmentation and eye recognition benchmarking competition

2017 IEEE International Joint Conference on Biometrics (IJCB)

Ferrer

et al. 2017

“…Sclera is the white region containing blood vessel patterns around the eyeball. Recently, as with other ocular biometric traits, sclera biometrics has increased in popularity [1][2][3][4][5][6][7][8][9][10][11]. Some recent investigations performed on multi-modal eye recognition (using iris and sclera) show that iris information fusion with sclera can enhance the applicability of iris biometrics in off-angle or off-axis eye gaze.…”

Section: Introductionmentioning

confidence: 99%

Sclera Vessel Pattern Synthesis Based on a Non-parametric Texture Synthesis Technique

Mondal

Advances in Intelligent Systems and Computing

et al. 2016

This work proposes a sclera vessel texture pattern synthesis technique. As it is difficult to produce a a large-scale collection of real sclera data, synthetic data can be a useful alternative for researchers. Sclera texture was synthesized by a non-parametric based texture regeneration technique. A small number of classes from the UBIRIS version: 1 dataset was employed as primitive images. An appreciable result was achieved which demonstrates the successful synthesis of sclera texture patterns.

“…It is evident from the literature that most of the individual work on the sclera [23,27] and multimodal eye recognition techniques [22,24,25] using the sclera and the iris employs a template matching-based technique for pattern classification, which is quite time consuming. Although in subsequent work of sclera biometrics [28][29][30][31][32] sophisticated classifier techniques such as Support Vector Machines are employed, however the time complexity of the feature extraction process is quite high.…”

Section: Introductionmentioning

confidence: 99%

Fast and efficent multimodal eye biometrics using projective dictionary pair learning

Mondal

2016 IEEE Congress on Evolutionary Computation (CEC)

et al. 2016

This work proposes a projective pairwise dictionary learning-based approach for fast and efficient multimodal eye biometrics. The work uses a faster Projective pairwise Discriminative Dictionary Learning (DL) in contrast to the traditional DL which uses synthesis DL. Projective Pairwise Discriminative Dictionary (PPDD) uses a synthesis dictionary and an analysis dictionary jointly to achieve the goal of pattern representation and discrimination. As the PPDD process of DL is in contrast to the use of l0 or l1-norm sparsity constraints on the representation coefficients adopted in most traditional DL, it works faster than other DL. Moreover, the blending of synthesis dictionary and an analysis dictionary also enhance the feature representation of the complex eye patterns. We employed the combination of sclera and iris traits to establish multimodal biometrics. The experimental study and analysis conducted fulfill the hypothesis we considered. In this work we employed a part of the UBIRIS version 1 dataset to conduct the experiments.