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@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{AccessMartin2023,

title = {ChildNet: Structural Kinship Face Synthesis Model With Appearance Control Mechanisms},

author = {Martin Pernuš and Mansi Bhatnagar and Badr Samad and Divyanshu Singh and Peter Peer and Vitomir Štruc and Simon Dobrišek},

url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10126110},

doi = {10.1109/ACCESS.2023.3276877},

issn = {2169-3536},

year  = {2023},

date = {2023-05-17},

journal = {IEEE Access},

pages = {1-22},

abstract = {Kinship face synthesis is an increasingly popular topic within the computer vision community, particularly the task of predicting the child appearance using parental images. Previous work has been limited in terms of model capacity and inadequate training data, which is comprised of low-resolution and tightly cropped images, leading to lower synthesis quality. In this paper, we propose ChildNet,  a method for kinship face synthesis that leverages the facial image generation capabilities of a state-of-the-art Generative Adversarial Network (GAN), and resolves the aforementioned problems. ChildNet is designed within the GAN latent space and is able to predict a child appearance that bears high resemblance to real parents’ children. To ensure fine-grained control, we propose an age and gender manipulation module that allows precise manipulation of the child synthesis result. ChildNet is capable of generating multiple child images per parent pair input, while providing a way to control the image generation variability. Additionally, we introduce a mechanism to control the dominant parent image. Finally, to facilitate the task of kinship face synthesis, we introduce a new kinship dataset, called Next of Kin. This dataset contains 3690 high-resolution face images with a diverse range of ethnicities and ages. We evaluate ChildNet in comprehensive experiments against three competing kinship face synthesis models, using two kinship datasets. The experiments demonstrate the superior performance of ChildNet in terms of identity similarity, while exhibiting high perceptual image quality. The source code for the model is publicly available at: https://github.com/MartinPernus/ChildNet.},

keywords = {artificial intelligence, CNN, deep learning, face generation, face synthesis, GAN, GAN inversion, kinship, kinship synthesis, StyleGAN2},

pubstate = {published},

tppubtype = {article}

}