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Shifeng Zhang; Ajian Liu; Jun Wan; Yanyan Liang; Guogong Guo; Sergio Escalera; Hugo Jair Escalante; Stan Z. Li |
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CASIA-SURF: A Dataset and Benchmark for Large-scale Multi-modal Face Anti-spoofing |
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2020 |
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IEEE Transactions on Biometrics, Behavior, and Identity Science |
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TTBIS |
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2 |
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2 |
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182 - 193 |
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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 (≤170) and modalities (≤2), 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 terms of both subjects and modalities. Specifically, it consists of 1,000 subjects with 21,000 videos and each sample has 3 modalities ( i.e. , RGB, Depth and IR). We also provide comprehensive evaluation metrics, diverse evaluation protocols, training/validation/testing subsets and a measurement tool, developing a new benchmark for face anti-spoofing. Moreover, we present a novel multi-modal multi-scale fusion method as a strong baseline, which performs feature re-weighting to select the more informative channel features while suppressing the less useful ones for each modality across different scales. Extensive experiments have been conducted on the proposed dataset to verify its significance and generalization capability. The dataset is available at https://sites.google.com/qq.com/face-anti-spoofing/welcome/challengecvpr2019?authuser=0 |
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HuPBA; no proj |
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no |
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Admin @ si @ ZLW2020 |
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3412 |
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Reza Azad; Maryam Asadi-Aghbolaghi; Shohreh Kasaei; Sergio Escalera |
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Title |
Dynamic 3D Hand Gesture Recognition by Learning Weighted Depth Motion Maps |
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2019 |
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IEEE Transactions on Circuits and Systems for Video Technology |
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TCSVT |
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29 |
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6 |
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1729-1740 |
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Hand gesture recognition; Multilevel temporal sampling; Weighted depth motion map; Spatio-temporal description; VLAD encoding |
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Hand gesture recognition from sequences of depth maps is a challenging computer vision task because of the low inter-class and high intra-class variability, different execution rates of each gesture, and the high articulated nature of human hand. In this paper, a multilevel temporal sampling (MTS) method is first proposed that is based on the motion energy of key-frames of depth sequences. As a result, long, middle, and short sequences are generated that contain the relevant gesture information. The MTS results in increasing the intra-class similarity while raising the inter-class dissimilarities. The weighted depth motion map (WDMM) is then proposed to extract the spatio-temporal information from generated summarized sequences by an accumulated weighted absolute difference of consecutive frames. The histogram of gradient (HOG) and local binary pattern (LBP) are exploited to extract features from WDMM. The obtained results define the current state-of-the-art on three public benchmark datasets of: MSR Gesture 3D, SKIG, and MSR Action 3D, for 3D hand gesture recognition. We also achieve competitive results on NTU action dataset. |
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June 2019, |
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HUPBA; no proj |
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Admin @ si @ AAK2018 |
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3213 |
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Chengyi Zou; Shuai Wan; Tiannan Ji; Marc Gorriz Blanch; Marta Mrak; Luis Herranz |
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Title |
Chroma Intra Prediction with Lightweight Attention-Based Neural Networks |
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2023 |
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IEEE Transactions on Circuits and Systems for Video Technology |
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TCSVT |
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34 |
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1 |
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549 - 560 |
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Neural networks can be successfully used for cross-component prediction in video coding. In particular, attention-based architectures are suitable for chroma intra prediction using luma information because of their capability to model relations between difierent channels. However, the complexity of such methods is still very high and should be further reduced, especially for decoding. In this paper, a cost-effective attention-based neural network is designed for chroma intra prediction. Moreover, with the goal of further improving coding performance, a novel approach is introduced to utilize more boundary information effectively. In addition to improving prediction, a simplification methodology is also proposed to reduce inference complexity by simplifying convolutions. The proposed schemes are integrated into H.266/Versatile Video Coding (VVC) pipeline, and only one additional binary block-level syntax flag is introduced to indicate whether a given block makes use of the proposed method. Experimental results demonstrate that the proposed scheme achieves up to −0.46%/−2.29%/−2.17% BD-rate reduction on Y/Cb/Cr components, respectively, compared with H.266/VVC anchor. Reductions in the encoding and decoding complexity of up to 22% and 61%, respectively, are achieved by the proposed scheme with respect to the previous attention-based chroma intra prediction method while maintaining coding performance. |
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MACO; LAMP |
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no |
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Admin @ si @ ZWJ2023 |
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3875 |
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Mingyi Yang; Fei Yang; Luka Murn; Marc Gorriz Blanch; Juil Sock; Shuai Wan; Fuzheng Yang; Luis Herranz |
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Title |
Task-Switchable Pre-Processor for Image Compression for Multiple Machine Vision Tasks |
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2024 |
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IEEE Transactions on Circuits and Systems for Video Technology |
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M Yang, F Yang, L Murn, MG Blanch, J Sock, S Wan, F Yang, L Herranz |
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Visual content is increasingly being processed by machines for various automated content analysis tasks instead of being consumed by humans. Despite the existence of several compression methods tailored for machine tasks, few consider real-world scenarios with multiple tasks. In this paper, we aim to address this gap by proposing a task-switchable pre-processor that optimizes input images specifically for machine consumption prior to encoding by an off-the-shelf codec designed for human consumption. The proposed task-switchable pre-processor adeptly maintains relevant semantic information based on the specific characteristics of different downstream tasks, while effectively suppressing irrelevant information to reduce bitrate. To enhance the processing of semantic information for diverse tasks, we leverage pre-extracted semantic features to modulate the pixel-to-pixel mapping within the pre-processor. By switching between different modulations, multiple tasks can be seamlessly incorporated into the system. Extensive experiments demonstrate the practicality and simplicity of our approach. It significantly reduces the number of parameters required for handling multiple tasks while still delivering impressive performance. Our method showcases the potential to achieve efficient and effective compression for machine vision tasks, supporting the evolving demands of real-world applications. |
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xxx |
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Admin @ si @ YYM2024 |
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4007 |
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Fadi Dornaika; Franck Davoine |
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On appearance based face and facial action tracking |
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2006 |
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IEEE Transactions on Circuits and Systems for Video Technology, 16(9): 1838–1853 |
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Admin @ si @ DoD2006b |
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732 |
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Zeynep Yucel; Albert Ali Salah; Çetin Meriçli; Tekin Meriçli; Roberto Valenti; Theo Gevers |
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Joint Attention by Gaze Interpolation and Saliency |
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2013 |
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IEEE Transactions on cybernetics |
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T-CIBER |
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43 |
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3 |
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829-842 |
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Joint attention, which is the ability of coordination of a common point of reference with the communicating party, emerges as a key factor in various interaction scenarios. This paper presents an image-based method for establishing joint attention between an experimenter and a robot. The precise analysis of the experimenter's eye region requires stability and high-resolution image acquisition, which is not always available. We investigate regression-based interpolation of the gaze direction from the head pose of the experimenter, which is easier to track. Gaussian process regression and neural networks are contrasted to interpolate the gaze direction. Then, we combine gaze interpolation with image-based saliency to improve the target point estimates and test three different saliency schemes. We demonstrate the proposed method on a human-robot interaction scenario. Cross-subject evaluations, as well as experiments under adverse conditions (such as dimmed or artificial illumination or motion blur), show that our method generalizes well and achieves rapid gaze estimation for establishing joint attention. |
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2168-2267 |
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ALTRES;ISE |
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no |
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Admin @ si @ YSM2013 |
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2363 |
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Alejandro Gonzalez Alzate; David Vazquez; Antonio Lopez; Jaume Amores |
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On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts |
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2017 |
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IEEE Transactions on cybernetics |
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Cyber |
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47 |
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11 |
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3980 - 3990 |
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Multicue; multimodal; multiview; object detection |
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Despite recent significant advances, object detection continues to be an extremely challenging problem in real scenarios. In order to develop a detector that successfully operates under these conditions, it becomes critical to leverage upon multiple cues, multiple imaging modalities, and a strong multiview (MV) classifier that accounts for different object views and poses. In this paper, we provide an extensive evaluation that gives insight into how each of these aspects (multicue, multimodality, and strong MV classifier) affect accuracy both individually and when integrated together. In the multimodality component, we explore the fusion of RGB and depth maps obtained by high-definition light detection and ranging, a type of modality that is starting to receive increasing attention. As our analysis reveals, although all the aforementioned aspects significantly help in improving the accuracy, the fusion of visible spectrum and depth information allows to boost the accuracy by a much larger margin. The resulting detector not only ranks among the top best performers in the challenging KITTI benchmark, but it is built upon very simple blocks that are easy to implement and computationally efficient. |
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2168-2267 |
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ADAS; 600.085; 600.082; 600.076; 600.118 |
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Admin @ si @ |
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2810 |
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Pau Rodriguez; Guillem Cucurull; Jordi Gonzalez; Josep M. Gonfaus; Kamal Nasrollahi; Thomas B. Moeslund; Xavier Roca |
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Title |
Deep Pain: Exploiting Long Short-Term Memory Networks for Facial Expression Classification |
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2017 |
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IEEE Transactions on cybernetics |
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Cyber |
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1-11 |
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Pain is an unpleasant feeling that has been shown to be an important factor for the recovery of patients. Since this is costly in human resources and difficult to do objectively, there is the need for automatic systems to measure it. In this paper, contrary to current state-of-the-art techniques in pain assessment, which are based on facial features only, we suggest that the performance can be enhanced by feeding the raw frames to deep learning models, outperforming the latest state-of-the-art results while also directly facing the problem of imbalanced data. As a baseline, our approach first uses convolutional neural networks (CNNs) to learn facial features from VGG_Faces, which are then linked to a long short-term memory to exploit the temporal relation between video frames. We further compare the performances of using the so popular schema based on the canonically normalized appearance versus taking into account the whole image. As a result, we outperform current state-of-the-art area under the curve performance in the UNBC-McMaster Shoulder Pain Expression Archive Database. In addition, to evaluate the generalization properties of our proposed methodology on facial motion recognition, we also report competitive results in the Cohn Kanade+ facial expression database. |
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ISE; 600.119; 600.098 |
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no |
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Admin @ si @ RCG2017a |
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2926 |
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Jun Wan; Chi Lin; Longyin Wen; Yunan Li; Qiguang Miao; Sergio Escalera; Gholamreza Anbarjafari; Isabelle Guyon; Guodong Guo; Stan Z. Li |
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Title |
ChaLearn Looking at People: IsoGD and ConGD Large-scale RGB-D Gesture Recognition |
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2022 |
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IEEE Transactions on Cybernetics |
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TCIBERN |
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52 |
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5 |
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3422-3433 |
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The ChaLearn large-scale gesture recognition challenge has been run twice in two workshops in conjunction with the International Conference on Pattern Recognition (ICPR) 2016 and International Conference on Computer Vision (ICCV) 2017, attracting more than 200 teams round the world. This challenge has two tracks, focusing on isolated and continuous gesture recognition, respectively. This paper describes the creation of both benchmark datasets and analyzes the advances in large-scale gesture recognition based on these two datasets. We discuss the challenges of collecting large-scale ground-truth annotations of gesture recognition, and provide a detailed analysis of the current state-of-the-art methods for large-scale isolated and continuous gesture recognition based on RGB-D video sequences. In addition to recognition rate and mean jaccard index (MJI) as evaluation metrics used in our previous challenges, we also introduce the corrected segmentation rate (CSR) metric to evaluate the performance of temporal segmentation for continuous gesture recognition. Furthermore, we propose a bidirectional long short-term memory (Bi-LSTM) baseline method, determining the video division points based on the skeleton points extracted by convolutional pose machine (CPM). Experiments demonstrate that the proposed Bi-LSTM outperforms the state-of-the-art methods with an absolute improvement of 8.1% (from 0.8917 to 0.9639) of CSR. |
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May 2022 |
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HUPBA; no menciona |
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Admin @ si @ WLW2022 |
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3522 |
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David Geronimo; Joan Serrat; Antonio Lopez; Ramon Baldrich |
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Traffic sign recognition for computer vision project-based learning |
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2013 |
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IEEE Transactions on Education |
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T-EDUC |
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56 |
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364-371 |
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traffic signs |
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This paper presents a graduate course project on computer vision. The aim of the project is to detect and recognize traffic signs in video sequences recorded by an on-board vehicle camera. This is a demanding problem, given that traffic sign recognition is one of the most challenging problems for driving assistance systems. Equally, it is motivating for the students given that it is a real-life problem. Furthermore, it gives them the opportunity to appreciate the difficulty of real-world vision problems and to assess the extent to which this problem can be solved by modern computer vision and pattern classification techniques taught in the classroom. The learning objectives of the course are introduced, as are the constraints imposed on its design, such as the diversity of students' background and the amount of time they and their instructors dedicate to the course. The paper also describes the course contents, schedule, and how the project-based learning approach is applied. The outcomes of the course are discussed, including both the students' marks and their personal feedback. |
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ADAS; CIC |
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Admin @ si @ GSL2013; ADAS @ adas @ |
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2160 |
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Xavier Otazu; M. Gonzalez-Audicana; O. Fors; J. Nuñez |
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Introduction of Sensor Spectral Response Into Image Fusion Methods. Application to Wavelet-Based Methods |
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2005 |
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IEEE Transactions on Geoscience and Remote Sensing, 43(10): 2376–2385 (IF: 1.627) |
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Anonymous |
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A Low Computational-Cost Method to Fuse IKONOS Images Using the Spectral Response Function of Its Sensors |
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IEEE Transactions on Geoscience and Remote Sensing, 44(6): 1683–1691 |
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J. Nuñez; O. Fors; Xavier Otazu; Vicenç Pala; Roman Arbiol; M.T. Merino |
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A Wavelet-Based Method for the Determination of the Relative Resolution Between Remotely Sensed Images |
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2006 |
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IEEE Transactions on Geoscience and Remote Sensing, 44(9): 2539–2548 |
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no |
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CAT @ cat @ NFO2006 |
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660 |
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Marc Bolaños; Mariella Dimiccoli; Petia Radeva |
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Title |
Towards Storytelling from Visual Lifelogging: An Overview |
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2017 |
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IEEE Transactions on Human-Machine Systems |
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THMS |
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47 |
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1 |
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77 - 90 |
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Visual lifelogging consists of acquiring images that capture the daily experiences of the user by wearing a camera over a long period of time. The pictures taken offer considerable potential for knowledge mining concerning how people live their lives, hence, they open up new opportunities for many potential applications in fields including healthcare, security, leisure and
the quantified self. However, automatically building a story from a huge collection of unstructured egocentric data presents major challenges. This paper provides a thorough review of advances made so far in egocentric data analysis, and in view of the current state of the art, indicates new lines of research to move us towards storytelling from visual lifelogging. |
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MILAB; 601.235 |
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Admin @ si @ BDR2017 |
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2712 |
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Judit Martinez; F. Thomas |
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Efficient Computation of Local Geometric Moments |
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2002 |
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IEEE Transactions on Image Porcessing, (IF: 2.553) |
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11 |
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9 |
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1102-1111 |
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Admin @ si @ MaT2002 |
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271 |
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