The widespread deployment of face recognition-based biometric systems has made face Presentation Attack Detection (face anti-spoofing) an increasingly critical issue. This survey thoroughly investigates the face Presentation Attack Detection (PAD) methods, that only require RGB cameras of generic consumer devices, over the past two decades. We present an attack scenario-oriented typology of the existing face PAD methods and we provide a review of over 50 of the most recent face PAD methods and their related issues. We adopt a comprehensive presentation of the methods that have most influenced face PAD following the proposed typology, and in chronological order. By doing so, we depict the main challenges, evolutions and current trends in the field of face PAD, and provide insights on its future research. From an experimental point of view, this survey paper provides a summarized overview of the available public databases and extensive comparative experimental results of different PAD methods.
University of Oulu（奥卢大学）；Northwestern Polytechnical University（西北工业大学）；Mininglamp Academy of Sciences, Mininglamp Technology（明略科技集团明略科学院）；Authenmetric（中科博宏）
Face anti-spoofing (FAS) plays a vital role in securing face recognition systems from presentation attacks. Existing multi-modal FAS methods rely on stacked vanilla convolutions, which is weak in describing detailed intrinsic information from modalities and easily being ineffective when the domain shifts (e.g., cross attack and cross ethnicity). In this paper, we extend the central difference convolutional networks (CDCN) \cite{yu2020searching} to a multi-modal version, intending to capture intrinsic spoofing patterns among three modalities (RGB, depth and infrared). Meanwhile, we also give an…
Beijing Jiaotong University（北京交通大学）；Universitat de Barcelona（巴塞罗那大学）；Computer Vision Center（计算机视觉中心）；Instituto Nacional de Astrofísica, Óptica y Electrónica（国家天体物理、光学与电子研究所）；Beihang University（北京航空航天大学）；Baidu Research（百度研究院）；Westlake University（西湖大学）
Face anti-spoofing is critical to prevent face recognition systems from a security breach. The biometrics community has %possessed achieved impressive progress recently due the excellent performance of deep neural networks and the availability of large datasets. Although ethnic bias has been verified to severely affect the performance of face recognition systems, it still remains an open research problem in face anti-spoofing. Recently, a multi-ethnic face anti-spoofing dataset, CASIA-SURF CeFA, has been released with the goal of measuring the ethnic bias. It is the largest up to date cross-et…
Idiap Research Institute（IDIAP研究所）；Rue Marconi 19, CH - 1920, Martigny, Switzerland（Idiap Research Institute（正确机构应为IDIAP研究所，原地址为补充信息））
Automatic methods for detecting presentation attacks are essential to ensure the reliable use of facial recognition technology. Most of the methods available in the literature for presentation attack detection (PAD) fails in generalizing to unseen attacks. In recent years, multi-channel methods have been proposed to improve the robustness of PAD systems. Often, only a limited amount of data is available for additional channels, which limits the effectiveness of these methods. In this work, we present a new framework for PAD that uses RGB and depth channels together with a novel loss function. …
JD Finance（京东金融）；Huawei Cloud（华为云）；Chinese Academy of Sciences（中国科学院）
Face anti-spoofing is significant to the security of face recognition systems. Previous works on depth supervised learning have proved the effectiveness for face anti-spoofing. Nevertheless, they only considered the depth as an auxiliary supervision in the single frame. Different from these methods, we develop a new method to estimate depth information from multiple RGB frames and propose a depth-supervised architecture which can efficiently encodes spatiotemporal information for presentation attack detection. It includes two novel modules: optical flow guided feature block (OFFB) and convolut…
School of Signal and Information Processing, Southeast University（东南大学信号与信息处理学院）；SenseTime Group Limited（商汤科技集团有限公司）
Face anti-spoofing is an important task to protect the security of face recognition. Most of previous work either struggle to capture discriminative and generalizable feature or rely on auxiliary information which is unavailable for most of industrial product. Inspired by the video classification work, we propose an efficient two-stream model to capture the key differences between live and spoof faces, which takes multi-frames and RGB difference as input respectively. Feature pyramid modules with two opposite fusion directions and pyramid pooling modules are applied to enhance feature represen…
Beihang University（北京航空航天大学）；Institute of Artificial Intelligence, Beihang University（北京航空航天大学人工智能研究院）；Hangzhou Innovation Institute, Beihang University（北京航空航天大学杭州创新研究院）
Along with the widespread use of face recognition systems, their vulnerability has become highlighted. While existing face anti-spoofing methods can be generalized between attack types, generic solutions are still challenging due to the diversity of spoof characteristics. Recently, the spoof trace disentanglement framework has shown great potential for coping with both seen and unseen spoof scenarios, but the performance is largely restricted by the single-modal input. This paper focuses on this issue and presents a multi-modal disentanglement model which targetedly learns polysemantic spoof t…
Center for Machine Vision and Signal Analysis（机器视觉与信号分析中心）；University of Oulu（奥卢大学）
Without deploying face anti-spoofing countermeasures, face recognition systems can be spoofed by presenting a printed photo, a video, or a silicon mask of a genuine user. Thus, face presentation attack detection (PAD) plays a vital role in providing secure facial access to digital devices. Most existing video-based PAD countermeasures lack the ability to cope with long-range temporal variations in videos. Moreover, the key-frame sampling prior to the feature extraction step has not been widely studied in the face anti-spoofing domain. To mitigate these issues, this paper provides a data sampli…
Wuhan University（武汉大学）；West Virginia University（西弗吉尼亚大学）
We have witnessed rapid advances in both face presentation attack models and presentation attack detection (PAD) in recent years. When compared with widely studied 2D face presentation attacks, 3D face spoofing attacks are more challenging because face recognition systems are more easily confused by the 3D characteristics of materials similar to real faces. In this work, we tackle the problem of detecting these realistic 3D face presentation attacks, and propose a novel anti-spoofing method from the perspective of fine-grained classification. Our method, based on factorized bilinear coding of …
Guangzhou Huya Information Technologies Co., Limited（广州虎牙信息科技有限公司）；University of Oxford（牛津大学）
Face anti-spoofing is crucial for the security of face recognition system, by avoiding invaded with presentation attack. Previous works have shown the effectiveness of using depth and temporal supervision for this task. However, depth supervision is often considered only in a single frame, and temporal supervision is explored by utilizing certain signals which is not robust to the change of scenes. In this work, motivated by two stream ConvNets, we propose a novel two stream FreqSaptialTemporalNet for face anti-spoofing which simultaneously takes advantage of frequent, spatial and temporal inf…
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Center for Research on Intelligent Perception and Computing（智能感知与计算研究中心）；Tianjin Academy for Intelligent Recognition Technologies（天津智能识别技术研究院）
Face anti-spoofing is the key to preventing security breaches in biometric recognition applications. Existing software-based and hardware-based face liveness detection methods are effective in constrained environments or designated datasets only. Deep learning method using RGB and infrared images demands a large amount of training data for new attacks. In this paper, we present a face anti-spoofing method in a real-world scenario by automatic learning the physical characteristics in polarization images of a real face compared to a deceptive attack. A computational framework is developed to ext…
SHIELD : An Evaluation Benchmark for Face Spoofing and Forgery Detection with Multimodal Large Language Models
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University（上海交通大学电子信息与电气工程学院）；Department of Artificial Intelligence, Xiamen University（厦门大学人工智能系）；School of Information Science and Technology, Shijiazhuang Tiedao University（石家庄铁道大学信息科学与技术学院）；Electrical Engineering Department, UCLA（加州大学洛杉矶分校电子工程系）；New Laboratory of Pattern Recognition(NLPR), Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所模式识别新实验室）；College of Electronics and Information Engineering, Shenzhen University（深圳大学电子与信息工程学院）；School of Computing and Information Technology, Great Bay University（广东东莞Great Bay大学计算与信息科技学院）；School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区网络科学与技术学院）
University of Oulu（奥卢大学）；Communication University of China（中国传媒大学）；SailYond Technology（赛扬德科技）；Institute of Automation, Chinese Academy of Sciences (CASIA)（中国科学院自动化研究所）；University of Chinese Academy of Sciences (UCAS)（中国科学院大学）；Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences（中国科学院香港创新研究院）
Face anti-spoofing (FAS) has lately attracted increasing attention due to its vital role in securing face recognition systems from presentation attacks (PAs). As more and more realistic PAs with novel types spring up, traditional FAS methods based on handcrafted features become unreliable due to their limited representation capacity. With the emergence of large-scale academic datasets in the recent decade, deep learning based FAS achieves remarkable performance and dominates this area. However, existing reviews in this field mainly focus on the handcrafted features, which are outdated and unin…
IFAST: Weakly Supervised Interpretable Face Anti-spoofing from Single-shot Binocular NIR Images
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；The Chinese University of Hong Kong（香港中文大学）
Great Bay University（大湾区大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；SailYond Technology（赛扬科技）；University of Oulu（奥卢大学）；Nanjing University of Information Science and Technology（南京信息工程大学）
Face anti-spoofing (FAS) plays a vital role in securing face recognition systems from presentation attacks. Benefitted from the maturing camera sensors, single-modal (RGB) and multi-modal (e.g., RGB+Depth) FAS has been applied in various scenarios with different configurations of sensors/modalities. Existing single- and multi-modal FAS methods usually separately train and deploy models for each possible modality scenario, which might be redundant and inefficient. Can we train a unified model, and flexibly deploy it under various modality scenarios? In this paper, we establish the first flexibl…
Face verification is a fast-growing authentication tool for everyday systems, such as smartphones. While current 2D face recognition methods are very accurate, it has been suggested recently that one may wish to add a 3D sensor to such solutions to make them more reliable and robust to spoofing, e.g., using a 2D print of a person's face. Yet, this requires an additional relatively expensive depth sensor. To mitigate this, we propose a novel authentication system, based on slim grayscale coded light field imaging. We provide a reconstruction free fast anti-spoofing mechanism, working directly o…
Nanyang Technological University（南洋理工大学）；City University of Hong Kong（香港城市大学）；South China University of Technology（华南理工大学）
Face Anti-Spoofing (FAS) is essential to secure face recognition systems and has been extensively studied in recent years. Although deep neural networks (DNNs) for the FAS task have achieved promising results in intra-dataset experiments with similar distributions of training and testing data, the DNNs' generalization ability is limited under the cross-domain scenarios with different distributions of training and testing data. To improve the generalization ability, recent hybrid methods have been explored to extract task-aware handcrafted features (e.g., Local Binary Pattern) as discriminative…
Special cameras that provide useful features for face anti-spoofing are desirable, but not always an option. In this work we propose a method to utilize the difference in dynamic appearance between bona fide and spoof samples by creating artificial modalities from RGB videos. We introduce two types of artificial transforms: rank pooling and optical flow, combined in end-to-end pipeline for spoof detection. We demonstrate that using intermediate representations that contain less identity and fine-grained features increase model robustness to unseen attacks as well as to unseen ethnicities. The …
Great Bay University（大湾区大学）；Nanyang Technological University（南洋理工大学）；University of Oulu（奥卢大学）；Lappeenranta-Lahti University of Technology（拉彭兰塔-拉赫蒂理工大学）；South China University of Technology（华南理工大学）
Recently, vision transformer (ViT) based multimodal learning methods have been proposed to improve the robustness of face anti-spoofing (FAS) systems. However, there are still no works to explore the fundamental natures (\textit{e.g.}, modality-aware inputs, suitable multimodal pre-training, and efficient finetuning) in vanilla ViT for multimodal FAS. In this paper, we investigate three key factors (i.e., inputs, pre-training, and finetuning) in ViT for multimodal FAS with RGB, Infrared (IR), and Depth. First, in terms of the ViT inputs, we find that leveraging local feature descriptors benefi…
While the performance of face recognition systems has improved significantly in the last decade, they are proved to be highly vulnerable to presentation attacks (spoofing). Most of the research in the field of face presentation attack detection (PAD), was focused on boosting the performance of the systems within a single database. Face PAD datasets are usually captured with RGB cameras, and have very limited number of both bona-fide samples and presentation attack instruments. Training face PAD systems on such data leads to poor performance, even in the closed-set scenario, especially when sop…
Alpha Reality Co（阿尔法现实公司）；IRAN Broadcasting University (IRIBU)（伊朗广播大学）
Face anti-spoofing aims at identifying the real face, as well as the fake one, and gains a high attention in security-sensitive applications, liveness detection, fingerprinting, and so on. In this paper, we address the anti-spoofing problem by proposing two end-to-end systems of convolutional neural networks. One model is developed based on the EfficientNet B0 network which has been modified in the final dense layers. The second one, is a very light model of the MobileNet V2, which has been contracted, modified and retrained efficiently on the data being created based on the Rose-Youtu dataset…
Université Laval（拉瓦尔大学）；University of St Andrews（圣安德鲁斯大学）
Determining the direction in which a person is looking is an important problem in a wide range of HCI applications. In this paper we describe a highly accurate algorithm that performs gaze estimation using an affordable and widely available device such as Kinect. The method we propose starts by performing accurate head pose estimation achieved by fitting a person specific morphable model of the face to depth data. The ordinarily competing requirements of high accuracy and high speed are met concurrently by formulating the fitting objective function as a combination of terms which excel either …
Regardless of the usage of deep learning and handcrafted methods, the dynamic information from videos and the effect of cross-ethnicity are rarely considered in face anti-spoofing. In this work, we propose a static-dynamic fusion mechanism for multi-modal face anti-spoofing. Inspired by motion divergences between real and fake faces, we incorporate the dynamic image calculated by rank pooling with static information into a conventional neural network (CNN) for each modality (i.e., RGB, Depth and infrared (IR)). Then, we develop a partially shared fusion method to learn complementary informatio…
JD AI Research（京东人工智能研究院）；MUST（澳门科技大学）；NLPR, CASIA, UCAS（中国科学院自动化研究所模式识别国家重点实验室、中国科学院大学）；Universitat de Barcelona, Computer Vision Center（巴塞罗那大学计算机视觉中心）；JD Finance（京东金融）
Face anti-spoofing is essential to prevent face recognition systems from a security breach. Much of the progresses have been made by the availability of face anti-spoofing benchmark datasets in recent years. However, existing face anti-spoofing benchmarks have limited number of subjects ($\le\negmedspace170$) and modalities ($\leq\negmedspace2$), which hinder the further development of the academic community. To facilitate face anti-spoofing research, we introduce a large-scale multi-modal dataset, namely CASIA-SURF, which is the largest publicly available dataset for face anti-spoofing in ter…
Ethnic bias has proven to negatively affect the performance of face recognition systems, and it remains an open research problem in face anti-spoofing. In order to study the ethnic bias for face anti-spoofing, we introduce the largest up to date CASIA-SURF Cross-ethnicity Face Anti-spoofing (CeFA) dataset (briefly named CeFA), covering $3$ ethnicities, $3$ modalities, $1,607$ subjects, and 2D plus 3D attack types. Four protocols are introduced to measure the affect under varied evaluation conditions, such as cross-ethnicity, unknown spoofs or both of them. To the best of our knowledge, CeFA is…
Instituto Maimónides de Investigación en Biomedicina (IMIBIC)（迈蒙尼德生物医学研究所）；Córdoba University（科尔多瓦大学）；Health Time；Clínica las Nieves（拉斯涅韦斯诊所）
BACKGROUND AND OBJECTIVE: Patient positioning is a crucial step in radiation therapy, for which non-invasive methods have been developed based on surface reconstruction using optical 3D imaging. However, most solutions need expensive specialized hardware and a careful calibration procedure that must be repeated over time.This paper proposes a fast and cheap patient positioning method based on inexpensive consumer level RGB-D sensors. METHODS: The proposed method relies on a 3D reconstruction approach that fuses, in real-time, artificial and natural visual landmarks recorded from a hand-held RG…
FM-ViT: Flexible Modal Vision Transformers for Face Anti-Spoofing
Institute of Automation, Chinese Academy of Sciences (CASIA)（中国科学院自动化研究所）；Baidu Research（百度研究院）；Great Bay University（大湾区大学）；Mininglamp Academy of Sciences, Mininglamp Technology（明略科技明略科学院）；Macau University of Science and Technology（澳门科技大学）；Westlake University（西湖大学）
Multi-Modal Face Anti-Spoofing via Cross-Modal Feature Transitions
College of Computer and Cyber Security, Fujian Normal University（计算机与网络安全学院，福建师范大学）；Department of Computer Science, National Tsing Hua University（计算机科学系，国立清华大学）
National Laboratory of Pattern Recognition (NLPR), Institute of Automation Chinese Academy of Sciences (CASIA) and University of Chinese Academy of Sciences (UCAS)（中国科学院自动化研究所（CASIA）模式识别国家重点实验室（NLPR）和中国科学院大学（UCAS））；Macau University of Science and Technology (MUST)（澳门科技大学（MUST））；Institute of Deep Learning, Baidu Research（百度研究院深度学习研究所）；National Engineering Laboratory for Deep Learning Technology and Application（深度学习技术及应用国家工程实验室）；Universitat de Barcelona (UB) and Computer Vision Center (CVC)（巴塞罗那大学（UB）和计算机视觉中心（CVC））；Instituto Nacional de Astrofísica, Óptica y Electrónica（国立天体物理、光学与电子研究所（INAOE））；Computer Science Department at CINVESTAV-Zacatenco（CINVESTAV-萨卡特科恩科计算机科学系）；Westlake University and Institute of Automation Chinese Academy of Sciences (CASIA)（西湖大学和中国科学院自动化研究所（CASIA））
Face anti-spoofing is essential to prevent face recognition systems from a security breach. Much of the progresses have been made by the availability of face anti-spoofing benchmark datasets in recent years. However, existing face anti-spoofing benchmarks have limited number of subjects ($\le\negmedspace170$) and modalities ($\leq\negmedspace2$), which hinder the further development of the academic community. To facilitate face anti-spoofing research, we introduce a large-scale multi-modal dataset, namely CASIA-SURF, which is the largest publicly available dataset for face anti-spoofing in ter…
In online markets, sellers can maliciously recapture others' images on display screens to utilize as spoof images, which can be challenging to distinguish in human eyes. To prevent such harm, we propose an anti-spoofing method using the paired rgb images and depth maps provided by the mobile camera with a Time-of-Fight sensor. When images are recaptured on display screens, various patterns differing by the screens as known as the moiré patterns can be also captured in spoof images. These patterns lead the anti-spoofing model to be overfitted and unable to detect spoof images recaptured on unse…
In this paper, a new method of training pipeline is discussed to achieve significant performance on the task of anti-spoofing with RGB image. We explore and highlight the impact of using pseudo-depth to pre-train a network that will be used as the backbone to the final classifier. While the usage of pseudo-depth for anti-spoofing task is not a new idea on its own, previous endeavours utilize pseudo-depth simply as another medium to extract features for performing prediction, or as part of many auxiliary losses in aiding the training of the main classifier, normalizing the importance of pseudo-…
DADM: Dual Alignment of Domain and Modality for Face Anti-spoofing
Great Bay University（大西洋大学）；University of Science and Technology of China（中国科学技术大学）；Dongguan Key Laboratory for Intelligence and Information Technology（东莞智能与信息技术重点实验室）；GRGBanking Equipment Co., Ltd.（GRGBanking设备有限公司）；South China University of Technology（华南理工大学）；Lappeenranta University of Technology（拉普兰塔理工大学）；Macao Polytechnic University（澳门理工学院）；Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区）
RGB Guided ToF Imaging System: A Survey of Deep Learning-based Methods
Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University（西安交通大学人工智能与机器人研究所）；University of Bologna（博洛尼亚大学）；Advanced Research Center on Electronic System (ARCES)（电子系统高级研究中心）
Intel IAGS SSP（英特尔IAGS SSP）；Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology（华中科技大学武汉光电国家研究中心）；Huazhong University of Science and Technology（华中科技大学）
Face Anti-spoofing gains increased attentions recently in both academic and industrial fields. With the emergence of various CNN based solutions, the multi-modal(RGB, depth and IR) methods based CNN showed better performance than single modal classifiers. However, there is a need for improving the performance and reducing the complexity. Therefore, an extreme light network architecture(FeatherNet A/B) is proposed with a streaming module which fixes the weakness of Global Average Pooling and uses less parameters. Our single FeatherNet trained by depth image only, provides a higher baseline with…
Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Delft University of Technology（代尔夫特理工大学）；The University of Manchester（曼彻斯特大学）
Reconstruction of high-fidelity 3D objects or scenes is a fundamental research problem. Recent advances in RGB-D fusion have demonstrated the potential of producing 3D models from consumer-level RGB-D cameras. However, due to the discrete nature and limited resolution of their surface representations (e.g., point- or voxel-based), existing approaches suffer from the accumulation of errors in camera tracking and distortion in the reconstruction, which leads to an unsatisfactory 3D reconstruction. In this paper, we present a method using on-the-fly implicits of Hermite Radial Basis Functions (HR…
SelfReDepth: Self-Supervised Real-Time Depth Restoration for Consumer-Grade Sensors
Instituto Superior Técnico, Universidade de Lisboa（里斯本大学高等技术学院）；Instituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento（系统与计算机工程研究所）；Institute for System and Robotics（系统与机器人研究所）；Human Technology Institute, University of Technology Sydney（悉尼科技大学人类技术学院）
Flow Augmentation and Knowledge Distillation for Lightweight Face Presentation Attack Detection
SDAIA-KFUPM Joint Research Center for Artificial Intelligence（SDAIA-KFUPM联合人工智能研究中心）；King Fahd University of Petroleum & Minerals（国王法赫德石油大学）；Interdisciplinary Research Center for Intelligent Secure Systems (IRC-ISS)（智能安全系统跨学科研究中心）；College of Information Science & Electronic Engineering（信息科学与电子工程学院）；Department of Computer Engineering, College of Computing and Mathematics（计算机工程系，计算与数学学院）
We propose a method to build in real-time animated 3D head models using a consumer-grade RGB-D camera. Our proposed method is the first one to provide simultaneously comprehensive facial motion tracking and a detailed 3D model of the user's head. Anyone's head can be instantly reconstructed and his facial motion captured without requiring any training or pre-scanning. The user starts facing the camera with a neutral expression in the first frame, but is free to move, talk and change his face expression as he wills otherwise. The facial motion is captured using a blendshape animation model whil…
Estimating the Diameter at Breast Height of Trees in a Forest from RGB
General Robotics, Automation, Sensing and Perception (GRASP) Laboratory, University of Pennsylvania（通用机器人、自动化、传感与感知实验室，宾夕法尼亚大学）；Department of Forest Resources and Environmental Conservation, Virginia Tech（森林资源与环境保护系，弗吉尼亚理工学院）
Beihang University（北京航空航天大学）；Harbin Institute of Technology（哈尔滨工业大学）
Planar grasp detection is one of the most fundamental tasks to robotic manipulation, and the recent progress of consumer-grade RGB-D sensors enables delivering more comprehensive features from both the texture and shape modalities. However, depth maps are generally of a relatively lower quality with much stronger noise compared to RGB images, making it challenging to acquire grasp depth and fuse multi-modal clues. To address the two issues, this paper proposes a novel learning based approach to RGB-D grasp detection, namely Depth Guided Cross-modal Attention Network (DGCAN). To better leverage…
IT+Robotics（IT+机器人公司）；University of Padua（帕多瓦大学）；Sapienza University of Rome（罗马第一大学）
Color-depth cameras (RGB-D cameras) have become the primary sensors in most robotics systems, from service robotics to industrial robotics applications. Typical consumer-grade RGB-D cameras are provided with a coarse intrinsic and extrinsic calibration that generally does not meet the accuracy requirements needed by many robotics applications (e.g., highly accurate 3D environment reconstruction and mapping, high precision object recognition and localization, ...). In this paper, we propose a human-friendly, reliable and accurate calibration framework that enables to easily estimate both the in…
De Montfort University（德蒙福特大学）；University of Lincoln（林肯大学）
In this paper, we propose mushrooms detection, localization and 3D pose estimation algorithm using RGB-D data acquired from a low-cost consumer RGB-D sensor. We use the RGB and depth information for different purposes. From RGB color, we first extract initial contour locations of the mushrooms and then provide both the initial contour locations and the original image to active contour for mushrooms segmentation. These segmented mushrooms are then used as input to a circular Hough transform for each mushroom detection including its center and radius. Once each mushroom's center position in the …
Kineo: Calibration-Free Metric Motion Capture From Sparse RGB Cameras
École Centrale de Lyon, CNRS, INSA Lyon, Université Claude Bernard Lyon 1, Université Lumière Lyon 2, LIRIS, UMR5205, ENISE（Lyon中央理工学院、国家科学研究中心、里昂国立应用科学学院、克莱门特-伯纳德大学里昂1分校、里昂2大学、LIRIS、UMR5205、ENISE）
RGB-D cameras have been successfully used for indoor High-ThroughpuT Phenotyping (HTTP). However, their capability and feasibility for in-field HTTP still need to be evaluated, due to the noise and disturbances generated by unstable illumination, specular reflection, and diffuse reflection, etc. To solve these problems, we evaluated the depth-ranging performances of two consumer-level RGB-D cameras (RealSense D435i and Kinect V2) under in-field HTTP scenarios, and proposed a strategy to compensate the depth measurement error. For performance evaluation, we focused on determining their optimal …
Towards Markerless Intraoperative Tracking of Deformable Spine Tissue
The Hamlyn Centre for Robotic Surgery, Imperial College London, London, UK, Department of Computing, Imperial College London, London, UK Department of Mechanical Engineering, Imperial College London, London, UK Department of Surgery \& Cancer, Imperial College London, London, UK Department of Neurosurgery, IRCCS Humanitas Research Hospital, Rozzano, Lombardy, Italy Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy. Stanford University School of Medicine, Stanford University, California, USA
Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；Saarland Informatics Campus（萨尔兰信息学园区）；EPFL（洛桑联邦理工学院）；University of British Columbia（不列颠哥伦比亚大学）
We present a real-time approach for multi-person 3D motion capture at over 30 fps using a single RGB camera. It operates successfully in generic scenes which may contain occlusions by objects and by other people. Our method operates in subsequent stages. The first stage is a convolutional neural network (CNN) that estimates 2D and 3D pose features along with identity assignments for all visible joints of all individuals.We contribute a new architecture for this CNN, called SelecSLS Net, that uses novel selective long and short range skip connections to improve the information flow allowing for…
A critical aspect of autonomous vehicles (AVs) is the object detection stage, which is increasingly being performed with sensor fusion models: multimodal 3D object detection models which utilize both 2D RGB image data and 3D data from a LIDAR sensor as inputs. In this work, we perform the first study to analyze the robustness of a high-performance, open source sensor fusion model architecture towards adversarial attacks and challenge the popular belief that the use of additional sensors automatically mitigate the risk of adversarial attacks. We find that despite the use of a LIDAR sensor, the …
Online hand gesture recognition (HGR) techniques are essential in augmented reality (AR) applications for enabling natural human-to-computer interaction and communication. In recent years, the consumer market for low-cost AR devices has been rapidly growing, while the technology maturity in this domain is still limited. Those devices are typical of low prices, limited memory, and resource-constrained computational units, which makes online HGR a challenging problem. To tackle this problem, we propose a lightweight and computationally efficient HGR framework, namely LE-HGR, to enable real-time …
Face Presentation Attack Detection via Content-Adaptive Spatial Operators
organization= Department of Computer Engineering, College of Computing；Mathematics, King Fahd University of Petroleum \& Minerals , city= Dhahran , postcode= 31261 , country= Saudi Arabia；organization= SDAIA--KFUPM Joint Research Center for Artificial Intelligence, King Fahd University of Petroleum \& Minerals , city= Dhahran , postcode= 31261 , country= Saudi Arabia
