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Beyond Error-vs-Discard Characteristic: Toward Stable and Reliable Evaluation for Face Image Quality Assessment
Bhavesh Wani, Žiga Babnik, Vitomir Štruc +1
Face Image Quality Assessment (FIQA) aims to estimate the utility of facial images for reliable recognition. The evaluation of FIQA methods is predominantly based on the Error-vers…
PreFIQs: Face Image Quality Is What Survives Pruning
Jan Niklas Kolf, Guray Ozgur, Andrea Atzori +4
Face Image Quality Assessment (FIQA) evaluates the utility of a face image for automated face recognition (FR) systems. In this work, we propose PreFIQs, an unsupervised and traini…
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…
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…
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…
Optimization-Based Improvement of Face Image Quality Assessment Techniques
Žiga Babnik, Naser Damer, Vitomir Štruc
Contemporary face recognition (FR) models achieve near-ideal recognition performance in constrained settings, yet do not fully translate the performance to unconstrained (realworld…
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…
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…
Assessing Bias in Face Image Quality Assessment
Žiga Babnik, Vitomir Štruc
Face image quality assessment (FIQA) attempts to improve face recognition (FR) performance by providing additional information about sample quality. Because FIQA methods attempt to…
Iterative Optimization of Pseudo Ground-Truth Face Image Quality Labels
Žiga Babnik, Vitomir Štruc
While recent face recognition (FR) systems achieve excellent results in many deployment scenarios, their performance in challenging real-world settings is still under question. For…