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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{KrizajEAAI2024,

title = {Deep Face Decoder: Towards understanding the embedding space of convolutional networks through visual reconstruction of deep face templates},

author = {Janez Križaj and Richard O. Plesh and Mahesh Banavar and Stephanie Schuckers and Vitomir Štruc},

url = {https://www.sciencedirect.com/science/article/abs/pii/S095219762400099X

https://lmi.fe.uni-lj.si/wp-content/uploads/2025/02/Deep_Face_Decoder__Elsevier_template_.pdf},

doi = {https://doi.org/10.1016/j.engappai.2024.107941},

year  = {2024},

date = {2024-01-30},

urldate = {2024-01-30},

journal = {Engineering Applications of Artificial Intelligence},

volume = {132},

issue = {107941},

pages = {1-20},

abstract = {Advances in deep learning and convolutional neural networks (ConvNets) have driven remarkable face recognition (FR) progress recently. However, the black-box nature of modern ConvNet-based face recognition models makes it challenging to interpret their decision-making process, to understand the reasoning behind specific success and failure cases, or to predict their responses to unseen data characteristics. It is, therefore, critical to design mechanisms that explain the inner workings of contemporary FR models and offer insight into their behavior. To address this challenge, we present in this paper a novel textit{template-inversion approach} capable of reconstructing high-fidelity face images from the embeddings (templates, feature-space representations) produced by modern FR techniques. Our approach is based on a novel Deep Face Decoder (DFD) trained in a regression setting to visualize the information encoded in the embedding space with the goal of fostering explainability. We utilize the developed DFD model in comprehensive experiments on multiple unconstrained face datasets, namely Visual Geometry Group Face dataset 2 (VGGFace2), Labeled Faces in the Wild (LFW), and Celebrity Faces Attributes Dataset High Quality (CelebA-HQ). Our analysis focuses on the embedding spaces of two distinct face recognition models with backbones based on the Visual Geometry Group 16-layer model (VGG-16) and the 50-layer Residual Network (ResNet-50). The results reveal how information is encoded in the two considered models and how perturbations in image appearance due to rotations, translations, scaling, occlusion, or adversarial attacks, are propagated into the embedding space. Our study offers researchers a deeper comprehension of the underlying mechanisms of ConvNet-based FR models, ultimately promoting advancements in model design and explainability. },

keywords = {CNN, embedding space, face, face images, face recognition, face synthesis, template reconstruction, xai},

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}

}