近年来，得益于深度卷积神经网络（CNN），人脸识别取得了显著进展。在许多人脸识别（FR）场景中，人脸图像采集自具有巨大类内变化的序列。这些主要受低质量人脸图像影响的类内变化会导致识别性能不稳定。以往工作侧重于采用临时性方法从视频中选择帧，或使用人脸图像质量评估（FIQA）方法，而这些方法只考虑某一种特定失真或若干种失真的组合。在本研究中，我们提出了一种用于FR的高效无参考图像质量评估方法，将图像质量评估（IQA）与FR直接联系起来。更具体地说，我们提出了一种无需任何参考即可评估图像质量的新度量。基于所提出的质量度量，我们提出了深度Tiny Face Quality网络（tinyFQnet），以从数据中学习质量预测函数。我们在两个经典的基于视频（或基于模板）的基准IJB-B和YTF上，针对不同的强大FR模型评估了所提方法。大量实验表明，尽管tinyFQnet比其他网络小得多，但所提方法在有效性和效率方面均优于最先进的质量评估方法。
英文摘要：
Face recognition has made significant progress in recent years due to deep convolutional neural networks (CNN). In many face recognition (FR) scenarios, face images are acquired from a sequence with huge intra-variations. These intra-variations, which are mainly affected by the low-quality face images, cause instability of recognition performance. Previous works have focused on ad-hoc methods to select frames from a video or use face image quality assessment (FIQA) methods, which consider only a particular or combination of several distortions.
In this work, we present an efficient non-reference image quality assessment for FR that directly links image quality assessment (IQA) and FR. More specifically, we propose a new measurement to evaluate image quality without any reference. Based on the proposed quality measurement, we propose a deep Tiny Face Quality network (tinyFQnet) to learn a quality prediction function from data.
We evaluate the proposed method for different powerful FR models on two classical video-based (or template-based) benchmark: IJB-B and YTF. Extensive experiments show that, although the tinyFQnet is much smaller than the others, the proposed method outperforms state-of-the-art quality assessment methods in terms of effectiveness and efficiency.
Retinal image quality assessment is an essential prerequisite for diagnosis of retinal diseases. Its goal is to identify retinal images in which anatomic structures and lesions attracting ophthalmologists' attention most are exhibited clearly and definitely while reject poor quality fundus images. Motivated by this, we mimic the way that ophthalmologists assess the quality of retinal images and propose a method termed SalStructuIQA. First, two salient structures for automated retinal quality assessment. One is the large-size salient structures including optic disc region and exudates in large-…
University of Science and Technology of China（中国科学技术大学）
Deep neural networks (DNNs) have shown great potential in non-reference image quality assessment (NR-IQA). However, the annotation of NR-IQA is labor-intensive and time-consuming, which severely limits their application especially for authentic images. To relieve the dependence on quality annotation, some works have applied unsupervised domain adaptation (UDA) to NR-IQA. However, the above methods ignore that the alignment space used in classification is sub-optimal, since the space is not elaborately designed for perception. To solve this challenge, we propose an effective perception-oriented…
A large portion of iris images captured in real world scenarios are poor quality due to the uncontrolled environment and the non-cooperative subject. To ensure that the recognition algorithm is not affected by low-quality images, traditional hand-crafted factors based methods discard most images, which will cause system timeout and disrupt user experience. In this paper, we propose a recognition-oriented quality metric and assessment method for iris image to deal with the problem. The method regards the iris image embeddings Distance in Feature Space (DFS) as the quality metric and the predict…
University of Science and Technology of China（中国科学技术大学）；Nanjing University of Information Science and Technology（南京信息工程大学）
Light field image (LFI) quality assessment is becoming more and more important, which helps to better guide the acquisition, processing and application of immersive media. However, due to the inherent high dimensional characteristics of LFI, the LFI quality assessment turns into a multi-dimensional problem that requires consideration of the quality degradation in both spatial and angular dimensions. Therefore, we propose a novel Tensor oriented No-reference Light Field image Quality evaluator (Tensor-NLFQ) based on tensor theory. Specifically, since the LFI is regarded as a low-rank 4D tensor,…
Perception-oriented Bidirectional Attention Network for Image Super-resolution Quality Assessment
School of Mathematical Sciences, Beihang University（北京航空航天大学数学学院）；School of Computer Science and Informatics, Cardiff University（卡迪夫大学计算机科学与信息学院）；School of Biomedical Engineering, Shenzhen University（深圳大学生物医学工程学院）；CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, University of Science and Technology of China（中国科学技术大学地球空间信息处理与应用系统国家重点实验室）；School of Information Science and Engineering, Ningbo University（宁波大学信息科学与工程学院）；School of Artificial Intelligence, Dalian University of Technology（大连理工大学人工智能学院）
Fuzhou University（福州大学）；Peng Cheng Laboratory（鹏城实验室）；Beijing University of Technology（北京工业大学）；Beijing Artificial Intelligence Institute（北京人工智能研究院）；Université de Nantes（南特大学）；Laboratoire des Sciences du Numérique de Nantes（南特数字科学实验室）
The widespread image applications have greatly promoted the vision-based tasks, in which the Image Quality Assessment (IQA) technique has become an increasingly significant issue. For user enjoyment in multimedia systems, the IQA exploits image fidelity and aesthetics to characterize user experience; while for other tasks such as popular object recognition, there exists a low correlation between utilities and perceptions. In such cases, the fidelity-based and aesthetics-based IQA methods cannot be directly applied. To address this issue, this paper proposes a utility-oriented IQA in object rec…
MMP-2K: A Benchmark Multi-Labeled Macro Photography Image Quality Assessment Database
Dundee International Institute of Central South University, Central South University, China（中南大学 Dundee 国际研究院，中国）；School of Computer Science, Northeast Electric Power University, China（东北电力大学计算机学院，中国）；School of Science and Engineering, University of Dundee, United Kingdom（邓迪大学科学与工程学院，英国）
Recent studies of deep learning based stereo image super-resolution (StereoSR) have promoted the development of StereoSR. However, existing StereoSR models mainly concentrate on improving quantitative evaluation metrics and neglect the visual quality of super-resolved stereo images. To improve the perceptual performance, this paper proposes the first perception-oriented stereo image super-resolution approach by exploiting the feedback, provided by the evaluation on the perceptual quality of StereoSR results. To provide accurate guidance for the StereoSR model, we develop the first special ster…
Quantitative Outcome-Oriented Assessment of Microsurgical Anastomosis
Learning Algorithms and Systems Laboratory (LASA), École Polytechnique Fédérale de Lausanne (EPFL), Switzerland.（学习算法与系统实验室（LASA），瑞士联邦理工学院（EPFL））；Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania.（伊利亚·哈蒂亚努大学医学与药学学院，罗马尼亚克鲁日-纳波卡）；Department of Neurosurgery, National Hospital of Denmark, Copenhagen, Denmark.（神经外科部门，丹麦国家医院，哥本哈根，丹麦）
ML-CLIPSim: Multi-Layer CLIP Similarity for Machine-Oriented Image Quality
Simon Fraser University（西蒙弗雷泽大学）；University of British Columbia（不列颠哥伦比亚大学）；Eastern Institute of Technology（东部技术学院）；Xi’an Jiaotong University（西安交通大学）；Google Inc（谷歌公司）
Image quality assessment (IQA) aims to assess the perceptual quality of images. The outputs of the IQA algorithms are expected to be consistent with human subjective perception. In image restoration and enhancement tasks, images generated by generative adversarial networks (GAN) can achieve better visual performance than traditional CNN-generated images, although they have spatial shift and texture noise. Unfortunately, the existing IQA methods have unsatisfactory performance on the GAN-based distortion partially because of their low tolerance to spatial misalignment. To this end, we propose t…
Pain assessment through observational pain scales is necessary for special categories of patients such as neonates, patients with dementia, critically ill patients, etc. The recently introduced Prkachin-Solomon score allows pain assessment directly from facial images opening the path for multiple assistive applications. In this paper, we introduce the Histograms of Topographical (HoT) features, which are a generalization of the topographical primal sketch, for the description of the face parts contributing to the mentioned score. We propose a semi-supervised, clustering oriented self--taught l…
OPPO Research Institute（OPPO研究院）；Xidian University（西安电子科技大学）
Personalized image aesthetics assessment (PIAA) is challenging due to its highly subjective nature. People's aesthetic tastes depend on diversified factors, including image characteristics and subject characters. The existing PIAA databases are limited in terms of annotation diversity, especially the subject aspect, which can no longer meet the increasing demands of PIAA research. To solve the dilemma, we conduct so far, the most comprehensive subjective study of personalized image aesthetics and introduce a new Personalized image Aesthetics database with Rich Attributes (PARA), which consists…
Digital humans are attracting more and more research interest during the last decade, the generation, representation, rendering, and animation of which have been put into large amounts of effort. However, the quality assessment of digital humans has fallen behind. Therefore, to tackle the challenge of digital human quality assessment issues, we propose the first large-scale quality assessment database for three-dimensional (3D) scanned digital human heads (DHHs). The constructed database consists of 55 reference DHHs and 1,540 distorted DHHs along with the subjective perceptual ratings. Then, …
LEHA-CVQAD: Dataset To Enable Generalized Video Quality Assessment of Compression Artifacts
ISP RAS Research Center for Trusted Artificial Intelligence（俄罗斯科学院信息与通信系统研究所可信人工智能研究中心）；Lomonosov Moscow State University（莫斯科罗蒙诺索夫莫斯科国立大学）；MSU Institute for Artificial Intelligence（莫斯科大学人工智能研究所）
A Deep Learning approach for Depressive Symptoms assessment in Parkinson's disease patients using facial videos
Foundation for Research and Technology Hellas (FORTH)（希腊研究与技术基金会）；University of Crete（克里特大学）；University Hospital of Heraklion（伊拉克利翁大学医院）；Patras University Hospital（帕特雷大学医院）；University of Ioannina（约阿尼纳大学）
Dataset Color Quantization: A Training-Oriented Framework for Dataset-Level Compression
CFAR, Agency for Science, Technology and Research, Singapore（科技研究局CFAR, 新加坡）；IHPC, Agency for Science, Technology and Research, Singapore（科技研究局IHPC, 新加坡）；National University of Singapore（新加坡国立大学）；University of Electronic Science and Technology of China (UESTC)（电子科技大学）；Nanyang Technological University (NTU), Singapore（南洋理工大学, 新加坡）
Multi-Modal Attention for Automated Disaster Damage Assessment Using Remote Sensing Imagery and Deep Learning
Built Environment Department, College of Science and Technology, North Carolina A&T State University（北卡罗来纳农工州立大学科技学院建筑环境系）；United Nations University Institute for Water, Environment and Health（联合国大学水、环境与健康研究所）
As the immersive multimedia techniques like Free-viewpoint TV (FTV) develop at an astonishing rate, user's demand for high-quality immersive contents increases dramatically. Unlike traditional uniform artifacts, the distortions within immersive contents could be non-uniform structure-related and thus are challenging for commonly used quality metrics. Recent studies have demonstrated that the representation of visual features can be extracted from multiple levels of the hierarchy. Inspired by the hierarchical representation mechanism in the human visual system (HVS), in this paper, we explore t…
Duke University（杜克大学）；Duke Clinical Research Institute（杜克临床研究所）；Duke-Margolis Center for Health Policy（杜克-马戈利斯卫生政策中心）；Duke Cancer Institute（杜克癌症研究所）
The assessment of knee osteoarthritis (KOA) severity on knee X-rays is a central criteria for the use of total knee arthroplasty. However, this assessment suffers from imprecise standards and a remarkably high inter-reader variability. An algorithmic, automated assessment of KOA severity could improve overall outcomes of knee replacement procedures by increasing the appropriateness of its use. We propose a novel deep learning-based five-step algorithm to automatically grade KOA from posterior-anterior (PA) views of radiographs: (1) image preprocessing (2) localization of knees joints in the im…
East China Normal University（华东师范大学）；Peter the Great St. Petersburg Polytechnic University（彼得大帝圣彼得堡理工大学）；Institute of Technical Physics, Chinese Academy of Sciences（中国科学院上海技术物理研究所）
During the rapid urbanization construction of China, acquisition of urban geographic information and timely data updating are important and fundamental tasks for the refined management of cities. With the development of domestic remote sensing technology, the application of Gaofen-2 (GF-2) high-resolution remote sensing images can greatly improve the accuracy of information extraction. This paper introduces an approach using object-oriented classification methods for urban feature extraction based on GF-2 satellite data. A combination of spectral, spatial attributes and membership functions wa…
