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@article{FadiIVCSynthetic,

title = {Synthetic data for face recognition: Current state and future prospects},

author = {Fadi Boutros and Vitomir Štruc and Julian Fierrez and Naser Damer},

url = {https://www.sciencedirect.com/science/article/pii/S0262885623000628},

doi = {https://doi.org/10.1016/j.imavis.2023.104688},

year  = {2023},

date = {2023-05-15},

urldate = {2023-05-15},

journal = {Image and Vision Computing},

number = {104688},

abstract = {Over the past years, deep learning capabilities and the availability of large-scale training datasets advanced rapidly, leading to breakthroughs in face recognition accuracy. However, these technologies are foreseen to face a major challenge in the next years due to the legal and ethical concerns about using authentic biometric data in AI model training and evaluation along with increasingly utilizing data-hungry state-of-the-art deep learning models. With the recent advances in deep generative models and their success in generating realistic and high-resolution synthetic image data, privacy-friendly synthetic data has been recently proposed as an alternative to privacy-sensitive authentic data to overcome the challenges of using authentic data in face recognition development. This work aims at providing a clear and structured picture of the use-cases taxonomy of synthetic face data in face recognition along with the recent emerging advances of face recognition models developed on the bases of synthetic data. We also discuss the challenges facing the use of synthetic data in face recognition development and several future prospects of synthetic data in the domain of face recognition.},

keywords = {biometrics, CNN, diffusion, face recognition, generative models, survey, synthetic data},

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{TIFS_PrivacySurvey,

title = {Privacy-Enhancing Face Biometrics: A Comprehensive Survey},

author = {Blaz Meden, Peter Rot, Philipp Terhorst, Naser Damer, Arjan Kuijper, Walter J. Scheirer, Arun Ross, Peter Peer, Vitomir Srruc},

url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9481149

https://lmi.fe.uni-lj.si/en/visual_privacy_of_faces__a_survey_preprint-compressed/},

doi = {10.1109/TIFS.2021.3096024},

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

volume = {vol. 16},

pages = {4147-4183},

abstract = {Biometric recognition technology has made significant advances over the last decade and is now used across a number of services and applications. However, this widespread deployment has also resulted in privacy concerns and evolving societal expectations about the appropriate use of the technology. For example, the ability to automatically extract age, gender, race, and health cues from biometric data has heightened concerns about privacy leakage. Face recognition technology, in particular, has been in the spotlight, and is now seen by many as posing a considerable risk to personal privacy. In response to these and similar concerns, researchers have intensified efforts towards developing techniques and computational models capable of ensuring privacy to individuals, while still facilitating the utility of face recognition technology in several application scenarios. These efforts have resulted in a multitude of privacy--enhancing techniques that aim at addressing privacy risks originating from biometric systems and providing technological solutions for legislative requirements set forth in privacy laws and regulations, such as GDPR. The goal of this overview paper is to provide a comprehensive introduction into privacy--related research in the area of biometrics and review existing work on textit{Biometric Privacy--Enhancing Techniques} (B--PETs) applied to face biometrics. To make this work useful for as wide of an audience as possible,  several key topics are covered as well, including evaluation strategies used with B--PETs, existing datasets, relevant standards, and regulations and critical open issues that will have to be addressed in the future. },

keywords = {B-PETs, biometrics, DEID, deidentification, face deidentification, face recognition, FaceGEN, overview, privacy, privacy-enhancing techniques, survey},

pubstate = {published},

tppubtype = {article}

}