papers

Publications (34)

cs.CV2026

PIU: Proximity-guided Identity Unlearning in ID-Conditioned Diffusion Models

Jose Edgar Hernandez Cancino Estrada, Mauro Díaz Lupone, Žiga Emeršič +3

Identity-conditioned diffusion models enable high-quality and identity-consistent face generation, but they also raise severe privacy concerns, as models may continue to synthesize…

cs.CV2025

EdgeEar: Efficient and Accurate Ear Recognition for Edge Devices

Camile Lendering, Bernardo Perrone Ribeiro, Žiga Emeršič +1

Ear recognition is a contactless and unobtrusive biometric technique with applications across various domains. However, deploying high-performing ear recognition models on resource…

cs.CV2023

Beyond Detection: Visual Realism Assessment of Deepfakes

Luka Dragar, Peter Peer, Vitomir Å truc +1

In the era of rapid digitalization and artificial intelligence advancements, the development of DeepFake technology has posed significant security and privacy concerns. This paper…

cs.CV2020

Recovery of Superquadrics from Range Images using Deep Learning: A Preliminary Study

Tim Oblak, Klemen Grm, Aleš Jaklič +3

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…

cs.CV2022

FaceQAN: Face Image Quality Assessment Through Adversarial Noise Exploration

Žiga Babnik, Peter Peer, Vitomir Štruc

Recent state-of-the-art face recognition (FR) approaches have achieved impressive performance, yet unconstrained face recognition still represents an open problem. Face image quali…

cs.CV2024

DiCTI: Diffusion-based Clothing Designer via Text-guided Input

Ajda Lampe, Julija Stopar, Deepak Kumar Jain +3

Recent developments in deep generative models have opened up a wide range of opportunities for image synthesis, leading to significant changes in various creative fields, including…

cs.CV2024

AI-KD: Towards Alignment Invariant Face Image Quality Assessment Using Knowledge Distillation

Žiga Babnik, Fadi Boutros, Naser Damer +2

Face Image Quality Assessment (FIQA) techniques have seen steady improvements over recent years, but their performance still deteriorates if the input face samples are not properly…

cs.CV2019

The Unconstrained Ear Recognition Challenge

Žiga Emeršič, Dejan Štepec, Vitomir Štruc +11

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 i…

cs.CV2022

C-VTON: Context-Driven Image-Based Virtual Try-On Network

Benjamin Fele, Ajda Lampe, Peter Peer +1

Image-based virtual try-on techniques have shown great promise for enhancing the user-experience and improving customer satisfaction on fashion-oriented e-commerce platforms. Howev…

cs.CV2026

FunFace: Feature Utility and Norm Estimation for Face Recognition

Žiga Babnik, Fadi Boutros, Naser Damer +3

Face Recognition (FR) is used in a variety of application domains, from entertainment and banking to security and surveillance. Such applications rely on the FR model to be robust…

cs.CV2019

Ear Recognition: More Than a Survey

Žiga Emeršič, Vitomir Štruc, Peter Peer

Automatic identity recognition from ear images represents an active field of research within the biometric community. The ability to capture ear images from a distance and in a cov…

cs.CV2022

Face Morphing Attack Detection Using Privacy-Aware Training Data

Marija Ivanovska, Andrej Kronovšek, Peter Peer +2

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 mor…

cs.CV2025

FROQ: Observing Face Recognition Models for Efficient Quality Assessment

Žiga Babnik, Deepak Kumar Jain, Peter Peer +1

Face Recognition (FR) plays a crucial role in many critical (high-stakes) applications, where errors in the recognition process can lead to serious consequences. Face Image Quality…

cs.CV2019

Pixel-wise Ear Detection with Convolutional Encoder-Decoder Networks

Žiga Emeršič, Luka Lan Gabriel, Vitomir Štruc +1

Object detection and segmentation represents the basis for many tasks in computer and machine vision. In biometric recognition systems the detection of the region-of-interest (ROI)…

cs.CV2025

SelfMAD: Enhancing Generalization and Robustness in Morphing Attack Detection via Self-Supervised Learning

Marija Ivanovska, Leon Todorov, Naser Damer +3

With the continuous advancement of generative models, face morphing attacks have become a significant challenge for existing face verification systems due to their potential use in…

cs.CV2016

Fine Hand Segmentation using Convolutional Neural Networks

Tadej Vodopivec, Vincent Lepetit, Peter Peer

We propose a method for extracting very accurate masks of hands in egocentric views. Our method is based on a novel Deep Learning architecture: In contrast with current Deep Learni…

cs.CV2025

Second Competition on Presentation Attack Detection on ID Card

Juan E. Tapia, Mario Nieto, Juan M. Espin +30

This work summarises and reports the results of the second Presentation Attack Detection competition on ID cards. This new version includes new elements compared to the previous on…

cs.CV2019

Simultaneous regression and feature learning for facial landmarking

Janez Križaj, Peter Peer, Vitomir Štruc +1

Face alignment (or facial landmarking) is an important task in many face-related applications, ranging from registration, tracking and animation to higher-level classification prob…

cs.CV2022

BiOcularGAN: Bimodal Synthesis and Annotation of Ocular Images

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 p…

cs.CV2022

Body Segmentation Using Multi-task Learning

Julijan Jug, Ajda Lampe, Vitomir Å truc +1

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. S…

cs.CV2019

The Unconstrained Ear Recognition Challenge 2019 - ArXiv Version With Appendix

Žiga Emeršič, Aruna Kumar S. V., B. S. Harish +28

This paper presents a summary of the 2019 Unconstrained Ear Recognition Challenge (UERC), the second in a series of group benchmarking efforts centered around the problem of person…

cs.CV2017

Face Deidentification with Generative Deep Neural Networks

Blaž Meden, Refik Can Mallı, Sebastjan Fabijan +3

Face deidentification is an active topic amongst privacy and security researchers. Early deidentification methods relying on image blurring or pixelization were replaced in recent…

cs.CV2025

MADation: Face Morphing Attack Detection with Foundation Models

Eduarda Caldeira, Guray Ozgur, Tahar Chettaoui +5

Despite the considerable performance improvements of face recognition algorithms in recent years, the same scientific advances responsible for this progress can also be used to cre…

cs.CV2022

Hierarchical Superquadric Decomposition with Implicit Space Separation

Jaka Å ircelj, Peter Peer, Franc Solina +1

We introduce a new method to reconstruct 3D objects using a set of volumetric primitives, i.e., superquadrics. The method hierarchically decomposes a target 3D object into pairs of…

cs.CV2025

Privacy-enhancing Sclera Segmentation Benchmarking Competition: SSBC 2025

Matej Vitek, Darian Tomašević, Abhijit Das +32

This paper presents a summary of the 2025 Sclera Segmentation Benchmarking Competition (SSBC), which focused on the development of privacy-preserving sclera-segmentation models tra…

cs.CV2022

SYN-MAD 2022: Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Data

Marco Huber, Fadi Boutros, Anh Thi Luu +16

This paper presents a summary of the Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Data (SYN-MAD) held at the 2022 International Joint Con…

cs.CV2019

Training Convolutional Neural Networks with Limited Training Data for Ear Recognition in the Wild

Žiga Emeršič, Dejan Štepec, Vitomir Štruc +1

Identity recognition from ear images is an active field of research within the biometric community. The ability to capture ear images from a distance and in a covert manner makes e…

cs.CV2025

ID-Booth: Identity-consistent Face Generation with Diffusion Models

Darian Tomašević, Fadi Boutros, Chenhao Lin +3

Recent advances in generative modeling have enabled the generation of high-quality synthetic data that is applicable in a variety of domains, including face recognition. Here, stat…

cs.CV2022

PrivacyProber: Assessment and Detection of Soft-Biometric Privacy-Enhancing Techniques

Peter Rot, Peter Peer, Vitomir Å truc

Soft-biometric privacy-enhancing techniques represent machine learning methods that aim to: (i) mitigate privacy concerns associated with face recognition technology by suppressing…

cs.CV2020

Segmentation and Recovery of Superquadric Models using Convolutional Neural Networks

Jaka Å ircelj, Tim Oblak, Klemen Grm +5

In this paper we address the problem of representing 3D visual data with parameterized volumetric shape primitives. Specifically, we present a (two-stage) approach built around con…

cs.CV2023

DifFIQA: Face Image Quality Assessment Using Denoising Diffusion Probabilistic Models

Žiga Babnik, Peter Peer, Vitomir Štruc

Modern face recognition (FR) models excel in constrained scenarios, but often suffer from decreased performance when deployed in unconstrained (real-world) environments due to unce…

cs.CV2022

GlassesGAN: Eyewear Personalization using Synthetic Appearance Discovery and Targeted Subspace Modeling

Richard Plesh, Peter Peer, Vitomir Å truc

We present GlassesGAN, a novel image editing framework for custom design of glasses, that sets a new standard in terms of image quality, edit realism, and continuous multi-style ed…

cs.CV2023

SeeABLE: Soft Discrepancies and Bounded Contrastive Learning for Exposing Deepfakes

Nicolas Larue, Ngoc-Son Vu, Vitomir Struc +2

Modern deepfake detectors have achieved encouraging results, when training and test images are drawn from the same data collection. However, when these detectors are applied to ima…

cs.CV2020

Influence of segmentation on deep iris recognition performance

Juš Lozej, Dejan Štepec, Vitomir Štruc +1

Despite the rise of deep learning in numerous areas of computer vision and image processing, iris recognition has not benefited considerably from these trends so far. Most of the e…