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@article{Vitek2024_Gaze,

title = {GazeNet: A lightweight multitask sclera feature extractor},

author = {Matej Vitek and Vitomir Štruc and Peter Peer},

url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2024/11/1-s2.0-S1110016824014273-main.pdf

https://www.sciencedirect.com/science/article/pii/S1110016824014273},

doi = {https://doi.org/10.1016/j.aej.2024.11.011},

year  = {2025},

date = {2025-01-05},

journal = {Alexandria Engineering Journal},

volume = {112},

pages = {661-671},

abstract = {The sclera is a recently emergent biometric modality with many desirable characteristics. However, most literature solutions for sclera-based recognition rely on sequences of complex deep networks with significant computational overhead. In this paper, we propose a lightweight multitask-based sclera feature extractor. The proposed GazeNet network has a computational complexity below 1 GFLOP, making it appropriate for less capable devices like smartphones and head-mounted displays. Our experiments show that GazeNet (which is based on the SqueezeNet architecture) outperforms both the base SqueezeNet model as well as the more computationally intensive ScleraNET model from the literature. Thus, we demonstrate that our proposed gaze-direction multitask learning procedure, along with careful lightweight architecture selection, leads to computationally efficient networks with high recognition performance.},

keywords = {biometrics, CNN, deep learning, lightweight models, sclera},

pubstate = {published},

tppubtype = {article}

}

@article{VitekSaud2023,

title = {IPAD: Iterative Pruning with Activation Deviation for Sclera Biometrics},

author = {Matej Vitek and Matic Bizjak and Peter Peer and Vitomir Štruc},

url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2023/07/PublishedVersion.pdf},

doi = {https://doi.org/10.1016/j.jksuci.2023.101630},

year  = {2023},

date = {2023-07-10},

journal = {Journal of King Saud University - Computer and Information Sciences},

volume = {35},

number = {8},

pages = {1-21},

abstract = {The sclera has recently been gaining attention as a biometric modality due to its various desirable characteristics. A key step in any type of ocular biometric recognition, including sclera recognition, is the segmentation of the relevant part(s) of the eye. However, the high computational complexity of the (deep) segmentation models used in this task can limit their applicability on resource-constrained devices such as smartphones or head-mounted displays. As these devices are a common desired target for such biometric systems, lightweight solutions for ocular segmentation are critically needed. To address this issue, this paper introduces IPAD (Iterative Pruning with Activation Deviation), a novel method for developing lightweight convolutional networks, that is based on model pruning. IPAD uses a novel filter-activation-based criterion (ADC) to determine low-importance filters and employs an iterative model pruning procedure to derive the final lightweight model. To evaluate the proposed pruning procedure, we conduct extensive experiments with two diverse segmentation models, over four publicly available datasets (SBVPI, SLD, SMD and MOBIUS), in four distinct problem configurations and in comparison to state-of-the-art methods from the literature. The results of the experiments show that the proposed filter-importance criterion outperforms the standard L1 and L2 approaches from the literature. Furthermore, the results also suggest that: 1) the pruned models are able to retain (or even improve on) the performance of the unpruned originals, as long as they are not over-pruned, with RITnet and U-Net at 50% of their original FLOPs reaching up to 4% and 7% higher IoU values than their unpruned versions, respectively, 2) smaller models require more careful pruning, as the pruning process can hurt the model’s generalization capabilities, and 3) the novel criterion most convincingly outperforms the classic approaches when sufficient training data is available, implying that the abundance of data leads to more robust activation-based importance computation.},

keywords = {biometrics, CNN, deep learning, model compression, pruning, sclera, sclera segmentation},

pubstate = {published},

tppubtype = {article}

}

@article{TIFS_Sclera2022,

title = {Exploring Bias in Sclera Segmentation Models: A Group Evaluation Approach},

author = {Matej Vitek and Abhijit Das and Diego Rafael Lucio and Luiz Antonio Zanlorensi Jr. and David Menotti and Jalil Nourmohammadi Khiarak and Mohsen Akbari Shahpar and Meysam Asgari-Chenaghlu and Farhang Jaryani and Juan E. Tapia and Andres Valenzuela and Caiyong Wang and Yunlong Wang and Zhaofeng He and Zhenan Sun and Fadi Boutros and Naser Damer and Jonas Henry Grebe and Arjan Kuijper and Kiran Raja and Gourav Gupta and Georgios Zampoukis and Lazaros Tsochatzidis and Ioannis Pratikakis and S. V. Aruna Kumar and B. S. Harish and Umapada Pal and Peter Peer and Vitomir Štruc},

url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9926136},

doi = {10.1109/TIFS.2022.3216468},

issn = {1556-6013},

year  = {2023},

date = {2023-01-18},

urldate = {2022-10-18},

journal = {IEEE Transactions on Information Forensics and Security},

volume = {18},

pages = {190-205},

abstract = {Bias and fairness of biometric algorithms have been key topics of research in recent years, mainly due to the societal, legal and ethical implications of potentially unfair decisions made by automated decision-making models. A considerable amount of work has been done on this topic across different biometric modalities, aiming at better understanding the main sources of algorithmic bias or devising mitigation measures. In this work, we contribute to these efforts and present the first study investigating bias and fairness of sclera segmentation models. Although sclera segmentation techniques represent a key component of sclera-based biometric systems with a considerable impact on the overall recognition performance, the presence of different types of biases in sclera segmentation methods is still underexplored. To address this limitation, we describe the results of a group evaluation effort (involving seven research groups), organized to explore the performance of recent sclera segmentation models within a common experimental framework and study performance differences (and bias), originating from various demographic as well as environmental factors. Using five diverse datasets, we analyze seven independently developed sclera segmentation models in different experimental configurations. The results of our experiments suggest that there are significant differences in the overall segmentation performance across the seven models and that among the considered factors, ethnicity appears to be the biggest cause of bias. Additionally, we observe that training with representative and balanced data does not necessarily lead to less biased results. Finally, we find that in general there appears to be a negative correlation between the amount of bias observed (due to eye color, ethnicity and acquisition device) and the overall segmentation performance, suggesting that advances in the field of semantic segmentation may also help with mitigating bias.},

keywords = {bias, biometrics, fairness, group evaluation, ocular, sclera, sclera segmentation, segmentation},

pubstate = {published},

tppubtype = {article}

}

@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}

}