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@article{Synth_InfoFUS2025,

title = {Second FRCSyn-onGoing: Winning solutions and post-challenge analysis to improve face recognition with synthetic data},

author = {Ivan DeAndres-Tame and Ruben Tolosana and Pietro Melzi and Ruben Vera-Rodriguez and Minchul Kim and Christian Rathgeb and Xiaoming Liu and Luis F. Gomez and Aythami Morales and Julian Fierrez and Javier Ortega-Garcia and Zhizhou Zhong and Yuge Huang and Yuxi Mi and Shouhong Ding and Shuigeng Zhou and Shuai He and Lingzhi Fu and Heng Cong and Rongyu Zhang and Zhihong Xiao and Evgeny Smirnov and Anton Pimenov and Aleksei Grigorev and Denis Timoshenko and Kaleb Mesfin Asfaw and Cheng Yaw Low and Hao Liu and Chuyi Wang and Qing Zuo and Zhixiang He and Hatef Otroshi Shahreza and Anjith George and Alexander Unnervik and Parsa Rahimi and Sebastien Marcel and Pedro C. Neto and Marco Huber and Jan Niklas Kolf and Naser Damer and Fadi Boutros and Jaime S. Cardoso and Ana F. Sequeira and Andrea Atzori and Gianni Fenu and Mirko Marras and Vitomir Štruc and Jiang Yu and Zhangjie Li and Jichun Li and Weisong Zhao and Zhen Lei and Xiangyu Zhu and Xiao-Yu Zhang and Bernardo Biesseck and Pedro Vidal and Luiz Coelho and Roger Granada and David Menotti},

url = {https://www.sciencedirect.com/science/article/pii/S1566253525001721},

doi = {https://doi.org/10.1016/j.inffus.2025.103099},

year  = {2025},

date = {2025-03-14},

urldate = {2025-03-14},

journal = {Information Fusion},

number = {103099},

abstract = {Synthetic data is gaining increasing popularity for face recognition technologies, mainly due to the privacy concerns and challenges associated with obtaining real data, including diverse scenarios, quality, and demographic groups, among others. It also offers some advantages over real data, such as the large amount of data that can be generated or the ability to customize it to adapt to specific problem-solving needs. To effectively use such data, face recognition models should also be specifically designed to exploit synthetic data to its fullest potential. In order to promote the proposal of novel Generative AI methods and synthetic data, and investigate the application of synthetic data to better train face recognition systems, we introduce the 2nd FRCSyn-on-Going challenge, based on the 2nd Face Recognition Challenge in the Era of Synthetic Data (FRCSyn), originally launched at CVPR 2024. This is an ongoing challenge that provides researchers with an accessible platform to benchmark (i) the proposal of novel Generative AI methods and synthetic data, and (ii) novel face recognition systems that are specifically proposed to take advantage of synthetic data. We focus on exploring the use of synthetic data both individually and in combination with real data to solve current challenges in face recognition such as demographic bias, domain adaptation, and performance constraints in demanding situations, such as age disparities between training and testing, changes in the pose, or occlusions. Very interesting findings are obtained in this second edition, including a direct comparison with the first one, in which synthetic databases were restricted to DCFace and GANDiffFace.},

keywords = {biometrics, data synthesis, face, face recognition, face synthesis, synthetic data},

pubstate = {published},

tppubtype = {article}

}

@article{Darian2024,

title = {Generating bimodal privacy-preserving data for face recognition},

author = {Darian Tomašević and Fadi Boutros and Naser Damer and Peter Peer and Vitomir Štruc},

url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2024/05/PapersDarian.pdf},

doi = {https://doi.org/10.1016/j.engappai.2024.108495},

year  = {2024},

date = {2024-05-01},

journal = {Engineering Applications of Artificial Intelligence},

volume = {133},

issue = {E},

pages = {1-25},

abstract = {The performance of state-of-the-art face recognition systems depends crucially on the availability of large-scale training datasets. However, increasing privacy concerns nowadays accompany the collection and distribution of biometric data, which has already resulted in the retraction of valuable face recognition datasets. The use of synthetic data represents a potential solution, however, the generation of privacy-preserving facial images useful for training recognition models is still an open problem. Generative methods also remain bound to the visible spectrum, despite the benefits that multispectral data can provide. To address these issues, we present a novel identity-conditioned generative framework capable of producing large-scale recognition datasets of visible and near-infrared privacy-preserving face images. The framework relies on a novel identity-conditioned dual-branch style-based generative adversarial network to enable the synthesis of aligned high-quality samples of identities determined by features of a pretrained recognition model. In addition, the framework incorporates a novel filter to prevent samples of privacy-breaching identities from reaching the generated datasets and improve both identity separability and intra-identity diversity. Extensive experiments on six publicly available datasets reveal that our framework achieves competitive synthesis capabilities while preserving the privacy of real-world subjects. The synthesized datasets also facilitate training more powerful recognition models than datasets generated by competing methods or even small-scale real-world datasets. Employing both visible and near-infrared data for training also results in higher recognition accuracy on real-world visible spectrum benchmarks. Therefore, training with multispectral data could potentially improve existing recognition systems that utilize only the visible spectrum, without the need for additional sensors.},

keywords = {CNN, face, face generation, face images, face recognition, generative AI, StyleGAN2, synthetic data},

pubstate = {published},

tppubtype = {article}

}

@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}

}