Vitomir Štruc
Full Professor, University of Ljubljana
Vitomir Štruc is a Full Professor at the University of Ljubljana, Slovenia. His research interests include problems related to biometrics, computer vision, image processing, and machine learning. He (co-)authored more than 200 research papers for leading international peer reviewed journals and conferences in these and related areas. Vitomir is a Deputy Editor-in-Chief for the IEEE Transactions on Information Forensics and Security, a Subject Editor for Elsevier’s Signal Processing and an Associate Editor for Pattern Recognition. He regularly serves on the organizing committees of visible international conferences, including IJCB, FG, WACV and CVPR. He was a Program Co-Chair for IJCB 2023, IEEE FG 2024 and WACV 2025, and currently acts as a Program Chair for IJCB 2025 and General Co-Chair for WACV 2026. Dr. Struc is a Senior member of the IEEE, a member of IAPR, EURASIP, Slovenia’s ambassador for the European Association for Biometrics (EAB) and the former president and current executive committee member of the Slovenian Pattern Recognition Society, the Slovenian member of IAPR. Vitomir is also the current VP Technical Activities for the IEEE Biometrics Council, the secretary of the IAPR Technical Committee on Biometrics (TC4) and a member of the Supervisory Board of the EAB.
Lecture
Face Image Quality Assessment (FIQA): Recent Advancements and Future Challenges
Understanding the quality characteristics of facial images is of key importance for various face-related applications, ranging from biometric verification systems and problems in surveillance and security to applications in human-machine interaction and affective computing. In this talk, I will first talk about the general problem of Face Image Quality Assessment (FIQA) and discuss how it differs from the more perceptually driven Image Quality Assessment (IQA) task that is used for quality assessment of arbitrary natural images. I will then elaborate on the most interesting trends and solutions towards face image quality assessment and present two of our recent models for this task, i.e., (i) FaceQAN that predicts quality based on the analysis of adversarial noise, and (ii) DifFIQA that uses probabilistic denoising diffusion models to estimate face image quality. Finally, I will share some insights with respect to FIQA techniques and highlight some open issues and future research directions in this space.
