2022 |
Tomašević, Darian; Peer, Peter; Štruc, Vitomir BiOcularGAN: Bimodal Synthesis and Annotation of Ocular Images Proceedings Article V: IEEE/IAPR International Joint Conference on Biometrics (IJCB 2022) , str. 1-10, 2022. Povzetek | Povezava | BibTeX | Oznake: 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 V: IEEE International Joint Conference on Biometrics (IJCB), str. 1-10, 2022, ISBN: 978-1-6654-6394-2. Povezava | BibTeX | Oznake: data synthesis, deep learning, face, face PAD, pad, synthetic data @inproceedings{IvanovskaSYNMAD, |
Objave
2022 |
BiOcularGAN: Bimodal Synthesis and Annotation of Ocular Images Proceedings Article V: IEEE/IAPR International Joint Conference on Biometrics (IJCB 2022) , str. 1-10, 2022. |
SYN-MAD 2022: Competition on Face Morphing Attack Detection based on Privacy-aware Synthetic Training Data Proceedings Article V: IEEE International Joint Conference on Biometrics (IJCB), str. 1-10, 2022, ISBN: 978-1-6654-6394-2. |