2 code implementations • 16 Apr 2024 • Ivan DeAndres-Tame, Ruben Tolosana, Pietro Melzi, Ruben Vera-Rodriguez, Minchul Kim, Christian Rathgeb, Xiaoming Liu, Aythami Morales, Julian Fierrez, Javier Ortega-Garcia, Zhizhou Zhong, Yuge Huang, Yuxi Mi, Shouhong Ding, Shuigeng Zhou, Shuai He, Lingzhi Fu, Heng Cong, Rongyu Zhang, Zhihong Xiao, Evgeny Smirnov, Anton Pimenov, Aleksei Grigorev, Denis Timoshenko, Kaleb Mesfin Asfaw, Cheng Yaw Low, Hao liu, Chuyi Wang, Qing Zuo, Zhixiang He, Hatef Otroshi Shahreza, Anjith George, Alexander Unnervik, Parsa Rahimi, Sébastien Marcel, Pedro C. Neto, Marco Huber, Jan Niklas Kolf, Naser Damer, Fadi Boutros, Jaime S. Cardoso, Ana F. Sequeira, Andrea Atzori, Gianni Fenu, Mirko Marras, Vitomir Štruc, Jiang Yu, Zhangjie Li, Jichun Li, Weisong Zhao, Zhen Lei, Xiangyu Zhu, Xiao-Yu Zhang, Bernardo Biesseck, Pedro Vidal, Luiz Coelho, Roger Granada, David Menotti
Synthetic data is gaining increasing relevance for training machine learning models.
2 code implementations • 15 Apr 2024 • Žiga Babnik, Fadi Boutros, Naser Damer, Peter Peer, Vitomir Štruc
To address this problem, we present in this paper a novel knowledge distillation approach, termed AI-KD that can extend on any existing FIQA technique, improving its robustness to alignment variations and, in turn, performance with different alignment procedures.
1 code implementation • 8 Aug 2023 • Jan Niklas Kolf, Fadi Boutros, Jurek Elliesen, Markus Theuerkauf, Naser Damer, Mohamad Alansari, Oussama Abdul Hay, Sara Alansari, Sajid Javed, Naoufel Werghi, Klemen Grm, Vitomir Štruc, Fernando Alonso-Fernandez, Kevin Hernandez Diaz, Josef Bigun, Anjith George, Christophe Ecabert, Hatef Otroshi Shahreza, Ketan Kotwal, Sébastien Marcel, Iurii Medvedev, Bo Jin, Diogo Nunes, Ahmad Hassanpour, Pankaj Khatiwada, Aafan Ahmad Toor, Bian Yang
To drive further development of efficient face recognition models, the submitted solutions are ranked based on a weighted score of the achieved verification accuracies on a diverse set of benchmarks, as well as the deployability given by the number of floating-point operations and model size.
no code implementations • 11 Jul 2023 • Marija Ivanovska, Vitomir Štruc
The detection of malicious deepfakes is a constantly evolving problem that requires continuous monitoring of detectors to ensure they can detect image manipulations generated by the latest emerging models.
1 code implementation • International Workshop on Biometrics and Forensics (IWBF) 2023 • Marija Ivanovska, Vitomir Štruc
Existing MAD techniques predominantly rely on discriminative models that learn from examples of bona fide and morphed images and, as a result, often exhibit sub-optimal generalization performance when confronted with unknown types of morphing attacks.
no code implementations • 9 Jun 2023 • Luka Dragar, Peter Peer, Vitomir Štruc, Borut Batagelj
In the era of rapid digitalization and artificial intelligence advancements, the development of DeepFake technology has posed significant security and privacy concerns.
1 code implementation • 24 May 2023 • Žiga Babnik, Naser Damer, Vitomir Štruc
To help improve the performance and stability of FR systems in such unconstrained settings, face image quality assessment (FIQA) techniques try to infer sample-quality information from the input face images that can aid with the recognition process.
1 code implementation • 9 May 2023 • Žiga Babnik, Peter Peer, Vitomir Štruc
In this paper, we present a powerful new FIQA approach, named DifFIQA, which relies on denoising diffusion probabilistic models (DDPM) and ensures highly competitive results.
1 code implementation • 5 Jan 2023 • Martin Pernuš, Clinton Fookes, Vitomir Štruc, Simon Dobrišek
We address these constraints by proposing a novel text-conditioned editing model, called FICE (Fashion Image CLIP Editing), capable of handling a wide variety of diverse text descriptions to guide the editing procedure.
no code implementations • 13 Dec 2022 • Julijan Jug, Ajda Lampe, Vitomir Štruc, Peter Peer
Body segmentation is an important step in many computer vision problems involving human images and one of the key components that affects the performance of all downstream tasks.
2 code implementations • 8 Dec 2022 • Benjamin Fele, Ajda Lampe, Peter Peer, Vitomir Štruc
At the core of the C-VTON pipeline are: (i) a geometric matching procedure that efficiently aligns the target clothing with the pose of the person in the input images, and (ii) a powerful image generator that utilizes various types of contextual information when synthesizing the final try-on result.
Ranked #6 on Virtual Try-on on VITON
1 code implementation • 5 Dec 2022 • Žiga Babnik, Peter Peer, Vitomir Štruc
In this paper, we propose a novel approach to face image quality assessment, called FaceQAN, that is based on adversarial examples and relies on the analysis of adversarial noise which can be calculated with any FR model learned by using some form of gradient descent.
no code implementations • 28 Nov 2022 • Klemen Grm, Berk Kemal Özata, Vitomir Štruc, Hazim Kemal Ekenel
In this paper, we aim to address the large domain gap between high-resolution face images, e. g., from professional portrait photography, and low-quality surveillance images, e. g., from security cameras.
no code implementations • 28 Nov 2022 • Žiga Babnik, Vitomir Štruc
Although modern face recognition systems are known to perform well, several studies have found that such systems often exhibit problems with demographic bias.
no code implementations • 16 Nov 2022 • Peter Rot, Peter Peer, Vitomir Štruc
Soft-biometric privacy-enhancing techniques represent machine learning methods that aim to: (i) mitigate privacy concerns associated with face recognition technology by suppressing selected soft-biometric attributes in facial images (e. g., gender, age, ethnicity) and (ii) make unsolicited extraction of sensitive personal information infeasible.
no code implementations • 7 Nov 2022 • Fevziye Irem Eyiokur, Alperen Kantarcı, Mustafa Ekrem Erakin, Naser Damer, Ferda Ofli, Muhammad Imran, Janez Križaj, Albert Ali Salah, Alexander Waibel, Vitomir Štruc, Hazim Kemal Ekenel
The emergence of COVID-19 has had a global and profound impact, not only on society as a whole, but also on the lives of individuals.
no code implementations • CVPR 2023 • Richard Plesh, Peter Peer, Vitomir Štruc
To facilitate the editing process with GlassesGAN, we propose a Targeted Subspace Modelling (TSM) procedure that, based on a novel mechanism for (synthetic) appearance discovery in the latent space of a pre-trained GAN generator, constructs an eyeglasses-specific (latent) subspace that the editing framework can utilize.
no code implementations • 15 Sep 2022 • Jaka Šircelj, Peter Peer, Franc Solina, Vitomir Štruc
We introduce a new method to reconstruct 3D objects using a set of volumetric primitives, i. e., superquadrics.
no code implementations • 31 Aug 2022 • Žiga Babnik, Vitomir Štruc
At ten iterations, the approach seems to perform the best, consistently outperforming the base quality scores of the three FIQA methods, chosen for the experiments.
1 code implementation • 15 Aug 2022 • Marco Huber, Fadi Boutros, Anh Thi Luu, Kiran Raja, Raghavendra Ramachandra, Naser Damer, Pedro C. Neto, Tiago Gonçalves, Ana F. Sequeira, Jaime S. Cardoso, João Tremoço, Miguel Lourenço, Sergio Serra, Eduardo Cermeño, Marija Ivanovska, Borut Batagelj, Andrej Kronovšek, Peter Peer, Vitomir Štruc
The competition attracted a total of 12 participating teams, both from academia and industry and present in 11 different countries.
no code implementations • 2 Jul 2022 • Marija Ivanovska, Andrej Kronovšek, Peter Peer, Vitomir Štruc, Borut Batagelj
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.
1 code implementation • 3 May 2022 • Darian Tomašević, Peter Peer, Vitomir Štruc
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.
no code implementations • 1 Apr 2022 • Miha Grabner, Yi Wang, Qingsong Wen, Boštjan Blažič, Vitomir Štruc
Efficient load forecasting is needed to ensure better observability in the distribution networks, whereas such forecasting is made possible by an increasing number of smart meter installations.
no code implementations • 24 Nov 2021 • Dailé Osorio-Roig, Christian Rathgeb, Pawel Drozdowski, Philipp Terhörst, Vitomir Štruc, Christoph Busch
Future works on privacy-enhancing face recognition are encouraged to include the proposed attack in evaluations on the privacy protection.
no code implementations • 28 Sep 2021 • Marija Ivanovska, Vitomir Štruc
Due to the separation of semantically-relevant and residual information, Y-GAN is able to derive informative data representations that allow for efficient anomaly detection across a diverse set of anomaly detection tasks.
no code implementations • 29 Jun 2021 • Fadi Boutros, Naser Damer, Jan Niklas Kolf, Kiran Raja, Florian Kirchbuchner, Raghavendra Ramachandra, Arjan Kuijper, Pengcheng Fang, Chao Zhang, Fei Wang, David Montero, Naiara Aginako, Basilio Sierra, Marcos Nieto, Mustafa Ekrem Erakin, Ugur Demir, Hazim Kemal, Ekenel, Asaki Kataoka, Kohei Ichikawa, Shizuma Kubo, Jie Zhang, Mingjie He, Dan Han, Shiguang Shan, Klemen Grm, Vitomir Štruc, Sachith Seneviratne, Nuran Kasthuriarachchi, Sanka Rasnayaka, Pedro C. Neto, Ana F. Sequeira, Joao Ribeiro Pinto, Mohsen Saffari, Jaime S. Cardoso
These teams successfully submitted 18 valid solutions.
2 code implementations • 20 Mar 2021 • Martin Pernuš, Vitomir Štruc, Simon Dobrišek
The proposed approach is based on an optimization procedure that directly optimizes the latent code of a pre-trained (state-of-the-art) Generative Adversarial Network (i. e., StyleGAN2) with respect to several constraints that ensure: (i) preservation of relevant image content, (ii) generation of the targeted facial attributes, and (iii) spatially--selective treatment of local image areas.
no code implementations • 28 Jan 2020 • Jaka Šircelj, Tim Oblak, Klemen Grm, Uroš Petković, Aleš Jaklič, Peter Peer, Vitomir Štruc, Franc Solina
In this paper we address the problem of representing 3D visual data with parameterized volumetric shape primitives.
no code implementations • 24 Apr 2019 • Janez Križaj, Peter Peer, Vitomir Štruc, Simon Dobrišek
We develop two distinct approaches around the proposed gating mechanism: i) the first uses a gated multiple ridge descent (GRID) mechanism in conjunction with established (hand-crafted) HOG features for face alignment and achieves state-of-the-art landmarking performance across a wide range of facial poses, ii) the second simultaneously learns multiple-descent directions as well as binary features (SMUF) that are optimal for the alignment tasks and in addition to competitive landmarking results also ensures extremely rapid processing.
no code implementations • 13 Apr 2019 • Tim Oblak, Klemen Grm, Aleš Jaklič, Peter Peer, Vitomir Štruc, Franc Solina
It has been a longstanding goal in computer vision to describe the 3D physical space in terms of parameterized volumetric models that would allow autonomous machines to understand and interact with their surroundings.
no code implementations • 11 Mar 2019 • Žiga Emeršič, Aruna Kumar S. V., B. S. Harish, Weronika Gutfeter, Jalil Nourmohammadi Khiarak, Andrzej Pacut, Earnest Hansley, Mauricio Pamplona Segundo, Sudeep Sarkar, Hyeonjung Park, Gi Pyo Nam, Ig-Jae Kim, Sagar G. Sangodkar, Ümit Kaçar, Murvet Kirci, Li Yuan, Jishou Yuan, Haonan Zhao, Fei Lu, Junying Mao, Xiaoshuang Zhang, Dogucan Yaman, Fevziye Irem Eyiokur, Kadir Bulut Özler, Hazim Kemal Ekenel, Debbrota Paul Chowdhury, Sambit Bakshi, Pankaj K. Sa, Banshidhar Majhi, Peter Peer, Vitomir Štruc
The goal of the challenge is to assess the performance of existing ear recognition techniques on a challenging large-scale ear dataset and to analyze performance of the technology from various viewpoints, such as generalization abilities to unseen data characteristics, sensitivity to rotations, occlusions and image resolution and performance bias on sub-groups of subjects, selected based on demographic criteria, i. e. gender and ethnicity.
no code implementations • 29 Jan 2019 • Juš Lozej, Dejan Štepec, Vitomir Štruc, Peter Peer
How important is the use of traditional segmentation methods in a deep learning setting?
no code implementations • 21 Dec 2018 • Klemen Grm, Martin Pernuš, Leo Cluzel, Walter Scheirer, Simon Dobrišek, Vitomir Štruc
This down-sampling (or degradation) procedure not only defines the characteristics of the LR training data, but also determines the type of image degradations the learned FH models are eventually able to handle.
no code implementations • 28 May 2018 • Klemen Grm, Simon Dobrišek, Walter J. Scheirer, Vitomir Štruc
In this paper we address the problem of hallucinating high-resolution facial images from unaligned low-resolution inputs at high magnification factors.
no code implementations • 27 Nov 2017 • Žiga Emeršič, Dejan Štepec, Vitomir Štruc, Peter Peer
The result of our work is the first CNN-based approach to ear recognition that is also made publicly available to the research community.
1 code implementation • 4 Oct 2017 • Klemen Grm, Vitomir Štruc, Anais Artiges, Matthieu Caron, Hazim Kemal Ekenel
However, studies systematically exploring the strengths and weaknesses of existing deep models for face recognition are still relatively scarce in the literature.
no code implementations • 23 Aug 2017 • Žiga Emeršič, Dejan Štepec, Vitomir Štruc, Peter Peer, Anjith George, Adil Ahmad, Elshibani Omar, Terrance E. Boult, Reza Safdari, Yuxiang Zhou, Stefanos Zafeiriou, Dogucan Yaman, Fevziye I. Eyiokur, Hazim K. Ekenel
In this paper we present the results of the Unconstrained Ear Recognition Challenge (UERC), a group benchmarking effort centered around the problem of person recognition from ear images captured in uncontrolled conditions.
no code implementations • 28 Jul 2017 • Blaž Meden, Refik Can Malli, Sebastjan Fabijan, Hazim Kemal Ekenel, Vitomir Štruc, Peter Peer
Our results show that the recognition performance on deidentified images is close to chance, suggesting that the deidentification process based on GNNs is highly effective.
no code implementations • 1 Feb 2017 • Žiga Emeršič, Luka Lan Gabriel, Vitomir Štruc, Peter Peer
For our technique, we formulate the problem of ear detection as a two-class segmentation problem and train a convolutional encoder-decoder network based on the SegNet architecture to distinguish between image-pixels belonging to either the ear or the non-ear class.
no code implementations • 18 Nov 2016 • Žiga Emeršič, Vitomir Štruc, Peter Peer
This paper presents an overview of the field of automatic ear recognition (from 2D images) and focuses specifically on the most recent, descriptor-based methods proposed in this area.