Face Image Quality Assessment (FIQA) is essential for reliable face recognition systems. Current approaches primarily exploit only final-layer representations, while training-free methods require multiple forward passes or backpropagation. We propose ViTNT-FIQA, a training-free approach that measures the stability of patch embedding evolution across intermediate Vision Transformer (ViT) blocks. We demonstrate that high-quality face images exhibit stable feature refinement trajectories across blocks, while degraded images show erratic transformations. Our method computes Euclidean distances between L2-normalized patch embeddings from consecutive transformer blocks and aggregates them into image-level quality scores. We empirically validate this correlation on a quality-labeled synthetic dataset with controlled degradation levels. Unlike existing training-free approaches, ViTNT-FIQA requires only a single forward pass without backpropagation or architectural modifications. Through extensive evaluation on eight benchmarks (LFW, AgeDB-30, CFP-FP, CALFW, Adience, CPLFW, XQLFW, IJB-C), we show that ViTNT-FIQA achieves competitive performance with state-of-the-art methods while maintaining computational efficiency and immediate applicability to any pre-trained ViT-based face recognition model.
IG-FIQA: Improving Face Image Quality Assessment through Intra-class Variance Guidance robust to Inaccurate Pseudo-Labels
Korea Institute of Sciene and Technology（韩国科学技术院）；Korea National University of Science and Technology（韩国科学技术大学）；Yonsei-KIST Convergence Research Institute（翰林-韩国科学技术院融合研究中心）
Guangzhou University（广州大学）；Tencent（腾讯）；Nanjing University of Science and Technology（南京理工大学）；Xiamen University（厦门大学）
In recent years, Face Image Quality Assessment (FIQA) has become an indispensable part of the face recognition system to guarantee the stability and reliability of recognition performance in an unconstrained scenario. For this purpose, the FIQA method should consider both the intrinsic property and the recognizability of the face image. Most previous works aim to estimate the sample-wise embedding uncertainty or pair-wise similarity as the quality score, which only considers the information from partial intra-class. However, these methods ignore the valuable information from the inter-class, w…
Fraunhofer Institute for Computer Graphics Research IGD（弗劳恩霍夫计算机图形学研究所IGD）；TU Darmstadt（达姆施塔特工业大学）
The quality of face images significantly influences the performance of underlying face recognition algorithms. Face image quality assessment (FIQA) estimates the utility of the captured image in achieving reliable and accurate recognition performance. In this work, we propose a novel learning paradigm that learns internal network observations during the training process. Based on that, our proposed CR-FIQA uses this paradigm to estimate the face image quality of a sample by predicting its relative classifiability. This classifiability is measured based on the allocation of the training sample …
Face image quality assessment (FIQA) attempts to improve face recognition (FR) performance by providing additional information about sample quality. Because FIQA methods attempt to estimate the utility of a sample for face recognition, it is reasonable to assume that these methods are heavily influenced by the underlying face recognition system. Although modern face recognition systems are known to perform well, several studies have found that such systems often exhibit problems with demographic bias. It is therefore likely that such problems are also present with FIQA techniques. To investiga…
DifFIQA: Face Image Quality Assessment Using Denoising Diffusion Probabilistic Models
University of Ljubljana, Faculty of Electrical Engineering（卢布尔雅那大学电气工程学院）；University of Ljubljana, Faculty of Computer and Information Science（卢布尔雅那大学计算机与信息科学学院）
A Lightweight Ensemble-Based Face Image Quality Assessment Method with Correlation-Aware Loss
DIEE, University of Cagliari, CNIT, University of Cagliari（卡利亚里大学DIEE部门，CNIT，卡利亚里大学）；Department of Information Technology (ITEC), Alpen-Adria Universität Klagenfurt（克雷格弗尔德大学信息科技学院（ITEC））
Univerza v Ljubljani, Fakulteta za Elektrotehniko（卢布尔雅那大学电气工程学院）
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 this reason, face image quality assessment (FIQA) techniques aim to support FR systems, by providing them with sample quality information that can be used to reject poor quality data unsuitable for recognition purposes. Several groups of FIQA methods relying on different concepts have been proposed in the literature, all of which can be used for generating quality scores of facial images that can serve as pseudo gro…
University of Ljubljana, Faculty of Electrical Engineering（卢布尔雅那大学电气工程学院）；University of Ljubljana, Faculty of Computer and Information Science（卢布尔雅那大学计算机与信息科学学院）
Recent state-of-the-art face recognition (FR) approaches have achieved impressive performance, yet unconstrained face recognition still represents an open problem. Face image quality assessment (FIQA) approaches aim to estimate the quality of the input samples that can help provide information on the confidence of the recognition decision and eventually lead to improved results in challenging scenarios. While much progress has been made in face image quality assessment in recent years, computing reliable quality scores for diverse facial images and FR models remains challenging. In this paper,…
Surveillance Facial Image Quality Assessment: A Multi-dimensional Dataset and Lightweight Model
Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University（上海交通大学图像通信与网络工程研究所）；School of Communication and Electronic Engineering, East China Normal University（东华大学通信与电子工程学院）；School of Computer Science and Technology, East China Normal University（东华大学计算机科学与技术学院）；Shanghai AI Lab（上海人工智能实验室）
Employing Vision-Language Models for Face Image Quality Assessment
Department of Computer Engineering, Istanbul Technical University（伊斯坦布尔技术大学计算机工程系）；Faculty of Electrical Engineering, University of Ljubljana（卢布尔雅那大学电气工程系）；Division of Engineering, NYU Abu Dhabi（纽约大学阿布扎克分校工程系）
FunPiQ: A New Benchmark for Pixel-Level Quality Assessment in Fundus Images
Institute of Artificial Intelligence, Center for Medical Data Science, Medical University of Vienna（维也纳医科大学医学数据科学中心人工智能研究所）；Christian Doppler Lab for Artificial Intelligence in Retina, Center for Medical Data Science, Medical University of Vienna（维也纳医科大学医学数据科学中心视网膜人工智能克里斯蒂安·多普勒实验室）
Fraunhofer Institute for Computer Graphics Research IGD（弗劳恩霍夫计算机图形研究所）；TU Darmstadt（达姆施塔特工业大学）
It is challenging to derive explainability for unsupervised or statistical-based face image quality assessment (FIQA) methods. In this work, we propose a novel set of explainability tools to derive reasoning for different FIQA decisions and their face recognition (FR) performance implications. We avoid limiting the deployment of our tools to certain FIQA methods by basing our analyses on the behavior of FR models when processing samples with different FIQA decisions. This leads to explainability tools that can be applied for any FIQA method with any CNN-based FR solution using activation mappi…
PreFIQs: Face Image Quality Is What Survives Pruning
Fraunhofer Institute for Computer Graphics Research IGD（弗劳恩霍夫计算机图形研究研究所IGD）；University of Ljubljana（卢布尔雅那大学）；Technical University of Darmstadt（达姆施塔特技术大学）
Towards Real-world Video Face Restoration: A New Benchmark
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Shanghai AI Laboratory（上海人工智能实验室）；The Chinese University of Hong Kong（香港中文大学）
The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Facebook Reality Labs（Facebook现实实验室）；NVIDIA（英伟达）
Previous blind or No Reference (NR) video quality assessment (VQA) models largely rely on features drawn from natural scene statistics (NSS), but under the assumption that the image statistics are stationary in the spatial domain. Several of these models are quite successful on standard pictures. However, in Virtual Reality (VR) applications, foveated video compression is regaining attention, and the concept of space-variant quality assessment is of interest, given the availability of increasingly high spatial and temporal resolution contents and practical ways of measuring gaze direction. Dis…
FunFace: Feature Utility and Norm Estimation for Face Recognition
University of Ljubljana（卢布尔雅纳大学）；Fraunhofer Institute for Computer Graphics Research IGD（弗劳恩霍夫计算机图形研究机构IGD）；Department of Computer Science, TU Darmstadt（图腾斯大学计算机科学系）；Dalian University of Technology（大连理工大学）
Towards Inclusive Face Recognition Through Synthetic Ethnicity Alteration
Sardar Vallabhbhai National Institute of Technology（萨达尔·瓦拉巴伊国家技术学院）；Norwegian University of Science and Technology（挪威科技大学）；State University of New York Polytechnic Institute（纽约州立大学理工学院）
