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@article{vitek2020comprehensive,

title = {A comprehensive investigation into sclera biometrics: a novel dataset and performance study},

author = {Matej Vitek and Peter Rot and Vitomir Struc and Peter Peer},

url = {https://link.springer.com/epdf/10.1007/s00521-020-04782-1},

doi = {https://doi.org/10.1007/s00521-020-04782-1},

year  = {2020},

date = {2020-01-01},

journal = {Neural Computing and Applications},

pages = {1-15},

abstract = {The area of ocular biometrics is among the most popular branches of biometric recognition technology. This area has long been dominated by iris recognition research, while other ocular modalities such as the periocular region or the vasculature of the sclera have received significantly less attention in the literature. Consequently, ocular modalities beyond the iris are not well studied and their characteristics are today still not as well understood. While recent needs for more secure authentication schemes have considerably increased the interest in competing ocular modalities, progress in these areas is still held back by the lack of publicly available datasets that would allow for more targeted research into specific ocular characteristics next to the iris. In this paper, we aim to bridge this gap for the case of sclera biometrics and introduce a novel dataset designed for research into ocular biometrics and most importantly for research into the vasculature of the sclera. Our dataset, called Sclera Blood Vessels, Periocular and Iris (SBVPI), is, to the best of our knowledge, the first publicly available dataset designed specifically with research in sclera biometrics in mind. The dataset contains high-quality RGB ocular images, captured in the visible spectrum, belonging to 55 subjects. Unlike competing datasets, it comes with manual markups of various eye regions, such as the iris, pupil, canthus or eyelashes and a detailed pixel-wise annotation of the complete sclera vasculature for a subset of the images. Additionally, the datasets ship with gender and age labels. The unique characteristics of the dataset allow us to study aspects of sclera biometrics technology that have not been studied before in the literature (e.g. vasculature segmentation techniques) as well as issues that are of key importance for practical recognition systems. Thus, next to the SBVPI dataset we also present in this paper a comprehensive investigation into sclera biometrics and the main covariates that affect the performance of sclera segmentation and recognition techniques, such as gender, age, gaze direction or image resolution. Our experiments not only demonstrate the usefulness of the newly introduced dataset, but also contribute to a better understanding of sclera biometrics in general.},

keywords = {biometrics, CNN, dataset, multi-view, ocular, performance study, recognition, sclera, segmentation, visible light},

pubstate = {published},

tppubtype = {article}

}

@article{emervsivc2017ear,

title = {Ear recognition: More than a survey},

author = {Žiga Emeršič and Vitomir Štruc and Peter Peer},

url = {https://arxiv.org/pdf/1611.06203.pdf},

year  = {2017},

date = {2017-01-01},

journal = {Neurocomputing},

volume = {255},

pages = {26--39},

publisher = {Elsevier},

abstract = {Automatic identity recognition from ear images represents an active field of research within the biometric community. The ability to capture ear images from a distance and in a covert manner makes the technology an appealing choice for surveillance and security applications as well as other application domains. Significant contributions have been made in the field over recent years, but open research problems still remain and hinder a wider (commercial) deployment of the technology. This paper presents an overview of the field of automatic ear recognition (from 2D images) and focuses specifically on the most recent, descriptor-based methods proposed in this area. Open challenges are discussed and potential research directions are outlined with the goal of providing the reader with a point of reference for issues worth examining in the future. In addition to a comprehensive review on ear recognition technology, the paper also introduces a new, fully unconstrained dataset of ear images gathered from the web and a toolbox implementing several state-of-the-art techniques for ear recognition. The dataset and toolbox are meant to address some of the open issues in the field and are made publicly available to the research community.},

keywords = {AWE, biometrics, dataset, ear, ear recognition, performance evalution, survey, toolbox},

pubstate = {published},

tppubtype = {article}

}

@article{Inform-Gajsek_2009,

title = {Multi-modal emotional database: AvID},

author = {Rok Gajšek and Vitomir Štruc and France Mihelič and Anja Podlesek and Luka Komidar and Gregor Sočan and Boštjan Bajec},

url = {https://lmi.fe.uni-lj.si/en/multi-modalemotionaldatabaseavid/},

year  = {2009},

date = {2009-01-01},

urldate = {2009-01-01},

journal = {Informatica (Ljubljana)},

volume = {33},

number = {1},

pages = {101-106},

abstract = {This paper presents our work on recording a multi-modal database containing emotional audio and video recordings. In designing the recording strategies a special attention was payed to gather data involving spontaneous emotions and therefore obtain a more realistic training and testing conditions for experiments. With specially planned scenarios including playing computer games and conducting an adaptive intelligence test different levels of arousal were induced. This will enable us to both detect different emotional states as well as experiment in speaker identification/verification of people involved in communications. So far the multi-modal database has been recorded and basic evaluation of the data was processed.},

keywords = {avid, database, dataset, emotion recognition, facial expression recognition, speech, speech technologies, spontaneous emotions},

pubstate = {published},

tppubtype = {article}

}