2023 |
Boutros, Fadi; Štruc, Vitomir; Fierrez, Julian; Damer, Naser Synthetic data for face recognition: Current state and future prospects Journal Article In: Image and Vision Computing, no. 104688, 2023. Abstract | Links | BibTeX | Tags: biometrics, CNN, diffusion, face recognition, generative models, survey, synthetic data @article{FadiIVCSynthetic,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. |
2022 |
Tomašević, Darian; Peer, Peter; Štruc, Vitomir BiOcularGAN: Bimodal Synthesis and Annotation of Ocular Images Proceedings Article In: IEEE/IAPR International Joint Conference on Biometrics (IJCB 2022) , pp. 1-10, 2022. Abstract | Links | BibTeX | Tags: biometrics, CNN, data synthesis, deep learning, ocular, segmentation, StyleGAN, synthetic data @inproceedings{TomasevicIJCBBiOcular,Current state-of-the-art segmentation techniques for ocular images are critically dependent on large-scale annotated datasets, which are labor-intensive to gather and often raise privacy concerns. In this paper, we present a novel framework, called BiOcularGAN, capable of generating synthetic large-scale datasets of photorealistic (visible light and near-infrared) ocular images, together with corresponding segmentation labels to address these issues. At its core, the framework relies on a novel Dual-Branch StyleGAN2 (DB-StyleGAN2) model that facilitates bimodal image generation, and a Semantic Mask Generator (SMG) component that produces semantic annotations by exploiting latent features of the DB-StyleGAN2 model. We evaluate BiOcularGAN through extensive experiments across five diverse ocular datasets and analyze the effects of bimodal data generation on image quality and the produced annotations. Our experimental results show that BiOcularGAN is able to produce high-quality matching bimodal images and annotations (with minimal manual intervention) that can be used to train highly competitive (deep) segmentation models (in a privacy aware-manner) that perform well across multiple real-world datasets. The source code for the BiOcularGAN framework is publicly available at: https://github.com/dariant/BiOcularGAN. |
Huber, Marco; Boutros, Fadi; Luu, Anh Thi; Raja, Kiran; Ramachandra, Raghavendra; Damer, Naser; Neto, Pedro C.; Goncalves, Tiago; Sequeira, Ana F.; Cardoso, Jaime S.; Tremoco, João; Lourenco, Miguel; Serra, Sergio; Cermeno, Eduardo; Ivanovska, Marija; Batagelj, Borut; Kronovšek, Andrej; Peer, Peter; Štruc, Vitomir SYN-MAD 2022: Competition on Face Morphing Attack Detection based on Privacy-aware Synthetic Training Data Proceedings Article In: IEEE International Joint Conference on Biometrics (IJCB), pp. 1-10, 2022, ISBN: 978-1-6654-6394-2. Links | BibTeX | Tags: data synthesis, deep learning, face, face PAD, pad, synthetic data @inproceedings{IvanovskaSYNMAD, |
Ivanovska, Marija; Kronovšek, Andrej; Peer, Peter; Štruc, Vitomir; Batagelj, Borut Face Morphing Attack Detection Using Privacy-Aware Training Data Proceedings Article In: Proceedings of ERK 2022, pp. 1-4, 2022. Abstract | Links | BibTeX | Tags: competition, face, face morphing, face morphing attack, face morphing detection, private data, synthetic data @inproceedings{MarijaMorphing,Images of morphed faces pose a serious threat to face recognition--based security systems, as they can be used to illegally verify the identity of multiple people with a single morphed image. Modern detection algorithms learn to identify such morphing attacks using authentic images of real individuals. This approach raises various privacy concerns and limits the amount of publicly available training data. In this paper, we explore the efficacy of detection algorithms that are trained only on faces of non--existing people and their respective morphs. To this end, two dedicated algorithms are trained with synthetic data and then evaluated on three real-world datasets, i.e.: FRLL-Morphs, FERET-Morphs and FRGC-Morphs. Our results show that synthetic facial images can be successfully employed for the training process of the detection algorithms and generalize well to real-world scenarios. |