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Facial expression recognition in the wild using improved dense trajectories and Fisher vector encoding 29

Facial expression recognition in the wild using improved den...

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29th ieee conference on computer vision and pattern recognition (cvpr)

作者： Afshar, Sadaf Salah, Albert Ali Bogazici Univ Computat Sci & Engn Program Istanbul Turkey Bogazici Univ Dept Comp Engn Istanbul Turkey

ISBN: (纸本)9781509014378

Improved dense trajectory features have been successfully used in video-based action recognition problems, but their application to face processing is more challenging. In this paper, we propose a novel system that deals with the problem of emotion recognition in real-world videos, using improved dense trajectory, LGBP-TOP, and geometric features. In the proposed system, we detect the face and facial landmarks from each frame of a video using a combination of two recent approaches, and register faces by means of Procrustes analysis. The improved dense trajectory and geometric features are encoded using Fisher vectors and classification is achieved by extreme learning machines. We evaluate our method on the extended Cohn-Kanade (CK+) and EmotiW 2015 Challenge databases. We obtain state-of-the-art results in both databases.

关键词： Trajectories

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Seeing the Forest from the Trees: A Holistic Approach to Near-infrared Heterogeneous Face recognition 29

Seeing the Forest from the Trees: A Holistic Approach to Nea...

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29th ieee conference on computer vision and pattern recognition (cvpr)

作者： Reale, Christopher Nasrabadi, Nasser M. Kwon, Heesung Chellappa, Rama US Army Res Lab Adelphi MD 20783 USA Univ Maryland College Pk MD 20742 USA West Virginia Univ Morgantown WV 26506 USA

ISBN: (纸本)9781509014378

Heterogeneous face recognition is the problem of identifying a person from a face image acquired with a non-traditional sensor by matching it to a visible gallery. Most approaches to this problem involve modeling the relationship between corresponding images from the visible and sensing domains. This is typically done at the patch level and/or with shallow models with the aim to prevent over-fitting. In this work, rather than modeling local patches or using a simple model, we propose to use a complex, deep model to learn the relationship between the entirety of cross-modal face images. We describe a deep convolutional neural network based method that leverages a large visible image face dataset to prevent overfitting. We present experimental results on two benchmark datasets showing its effectiveness.

关键词： Face recognition

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Effectiveness of Grasp Attributes and Motion-Constraints for Fine-Grained recognition of Object Manipulation Actions 29

Effectiveness of Grasp Attributes and Motion-Constraints for...

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29th ieee conference on computer vision and pattern recognition (cvpr)

作者： Gupta, Kartik Burschka, Darius Bhaysar, Army IIT Mandi Sch Comp & Elect Engn Mandi India Tech Univ Munich Dept Comp Sci Munich Germany

ISBN: (纸本)9781509014378

In this work, we consider the problem of recognition of object manipulation actions. This is a challenging task for real everyday actions, as the same object can be grasped and moved in different ways depending on its functions and geometric constraints of the task. We propose to leverage grasp and motion-constraints information, using a suitable representation, to recognize and understand action intention with different objects. We also provide an extensive experimental evaluation on the recent Yale Human Grasping dataset consisting of large set of 455 manipulation actions. The evaluation involves a) Different contemporary multi-class classifiers, and binary classifiers with one-vs-one multi-class voting scheme, and b) Differential comparisons results based on subsets of attributes involving information of grasp and motion-constraints. Our results clearly demonstrate the usefulness of grasp characteristics and motion-constraints, to understand actions intended with an object.

关键词： Classification (of information)

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Embedding Sequential Information into Spatiotemporal Features for Action recognition 29

Embedding Sequential Information into Spatiotemporal Feature...

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29th ieee conference on computer vision and pattern recognition (cvpr)

作者： Ye, Yuancheng Tian, Yingli CUNY Grad Ctr New York NY 10021 USA CUNY City Coll New York NY 10021 USA

ISBN: (纸本)9781509014378

In this paper, we introduce a novel framework for video-based action recognition, which incorporates the sequential information with the spatiotemporal features. Specifically, the spatiotemporal features are extracted from the sliced clips of videos, and then a recurrent neural network is applied to embed the sequential information into the final feature representation of the video. In contrast to most current deep learning methods for the video-based tasks, our framework incorporates both long-term dependencies and spatiotemporal information of the clips in the video. To extract the spatiotemporal features from the clips, both dense trajectories (DT) and a newly proposed 3D neural network, C3D, are applied in our experiments. Our proposed framework is evaluated on the benchmark datasets of UCF101 and HMDB51, and achieves comparable performance compared with the state-of-the-art results.

关键词： Recurrent neural networks

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Skeleton-based Dynamic hand gesture recognition 29

Skeleton-based Dynamic hand gesture recognition

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29th ieee conference on computer vision and pattern recognition (cvpr)

作者： De Smedt, Quentin Wannous, Hazem Vandeborre, Jean-Philippe Univ Lille Telecom Lille CNRS UMR 9189CRIStAL F-59000 Lille France

ISBN: (纸本)9781509014378

In this paper, a new skeleton-based approach is proposed for 3D hand gesture recognition. Specifically, we exploit the geometric shape of the hand to extract an effective descriptor from hand skeleton connected joints returned by the Intel RealSense depth camera. Each descriptor is then encoded by a Fisher Vector representation obtained using a Gaussian Mixture Model. A multi-level representation of Fisher Vectors and other skeleton-based geometric features is guaranteed by a temporal pyramid to obtain the final feature vector, used later to achieve the classification by a linear SVM classifier. The proposed approach is evaluated on a challenging hand gesture dataset containing 14 gestures, performed by 20 participants performing the same gesture with two different numbers of fingers. Experimental results show that our skeleton-based approach consistently achieves superior performance over a depth-based approach.

关键词： Gaussian distribution

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Multimodal Multi-stream Deep Learning for Egocentric Activity recognition 29

Multimodal Multi-stream Deep Learning for Egocentric Activit...

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29th ieee conference on computer vision and pattern recognition (cvpr)

作者： Song, Sibo Chandrasekhar, Vijay Mandal, Bappaditya Li, Liyuan Lim, Joo-Hwee Babu, Giduthuri Sateesh San, Phyo Phyo Cheung, Ngai-Man Singapore Univ Technol & Design Singapore Singapore Inst Infocomm Res Singapore Singapore

ISBN: (纸本)9781509014378

In this paper, we propose a multimodal multi-stream deep learning framework to tackle the egocentric activity recognition problem, using both the video and sensor data. First, we experiment and extend a multi-stream Convolutional Neural Network to learn the spatial and temporal features from egocentric videos. Second, we propose a multi-stream Long Short-Term Memory architecture to learn the features from multiple sensor streams (accelerometer, gyroscope, etc.). Third, we propose to use a two-level fusion technique and experiment different pooling techniques to compute the prediction results. Experimental results using a multimodal egocentric dataset show that our proposed method can achieve very encouraging performance, despite the constraint that the scale of the existing egocentric datasets is still quite limited.

关键词： Memory architecture

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Kernel ELM and CNN based Facial Age Estimation 29

Kernel ELM and CNN based Facial Age Estimation

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29th ieee conference on computer vision and pattern recognition (cvpr)

作者： Gurpinar, Furkan Kaya, Heysem Dibeklioglu, Hamdi Salah, Albert Ali Bogazici Univ Program Computat Sci & Engn Istanbul Turkey Namik Kemal Univ Dept Comp Engn Tekirdag Turkey Delft Univ Technol Pattern Recognit & Bioinformat Grp Delft Netherlands Bogazici Univ Dept Comp Engn Istanbul Turkey

ISBN: (纸本)9781509014378

e propose a two-level system for apparent age estimation from facial images. Our system first classifies samples into overlapping age groups. Within each group, the apparent age is estimated with local regressors, whose outputs are then fused for the final estimate. We use a deformable parts model based face detector, and features from a pre-trained deep convolutional network. Kernel extreme learning machines are used for classification. We evaluate our system on the ChaLearn Looking at People 2016 - Apparent Age Estimation challenge dataset, and report 0.3740 normal score on the sequestered test set.

关键词： Learning systems

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Background Subtraction using Local SVD Binary pattern 29

Background Subtraction using Local SVD Binary Pattern

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29th ieee conference on computer vision and pattern recognition (cvpr)

作者： Guo, Lili Xu, Dan Qiang, Zhenping Yunnan Univ Sch Informat & Engn Kunming Peoples R China Southwest Forestry Univ Kunming Yunnan Peoples R China

ISBN: (纸本)9781509014378

Background subtraction is a basic problem for change detection in videos and also the first step of high-level computer vision applications. Most background subtraction methods rely on color and texture feature. However, due to illuminations changes in different scenes and affections of noise pixels, those methods often resulted in high false positives in a complex environment. To solve this problem, we propose an adaptive background subtraction model which uses a novel Local SVD Binary pattern (named LSBP) feature instead of simply depending on color intensity. This feature can describe the potential structure of the local regions in a given image, thus, it can enhance the robustness to illumination variation, noise, and shadows. We use a sample consensus model which is well suited for our LSBP feature. Experimental results on CDnet 2012 dataset demonstrate that our background subtraction method using LSBP feature is more effective than many state-of-the-art methods.

关键词： Image enhancement

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Dominant Codewords Selection with Topic Model for Action recognition 29

Dominant Codewords Selection with Topic Model for Action Rec...

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29th ieee conference on computer vision and pattern recognition (cvpr)

作者： Kataoka, Hirokatsu Iwata, Kenji Satoh, Yutaka Hayashi, Masaki Aoki, Yoshimitsu Ilic, Slobodan Natl Inst Adv Ind Sci & Technol Tsukuba Ibaraki Japan Keio Univ Yokohama Kanagawa Japan Tech Univ Munich Munich Germany

ISBN: (纸本)9781509014378

In this paper, we propose a framework for recognizing human activities that uses only in-topic dominant code-words and a mixture of intertopic vectors. Latent Dirichlet allocation (LDA) is used to develop approximations of human motion primitives;these are mid-level representations, and they adaptively integrate dominant vectors when classifying human activities. In LDA topic modeling, action videos (documents) are represented by a bag-of-words (input from a dictionary), and these are based on improved dense trajectories ([18]). The output topics correspond to human motion primitives, such as finger moving or subtle leg motion. We eliminate the impurities, such as missed tracking or changing light conditions, in each motion primitive. The assembled vector of motion primitives is an improved representation of the action. We demonstrate our method on four different datasets.

关键词： Statistics

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Human object interaction recognition using rate-invariant shape analysis of inter joint distances trajectories 29

Human object interaction recognition using rate-invariant sh...

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29th ieee conference on computer vision and pattern recognition (cvpr)

作者： Meng, Meng Drira, Hassen Daoudi, Mohamed Boonaert, Jacques Telecom Lille CRIStAL Lab UMR CNRS 9189 F-9189 Lille France Ecole Mines Douai Douai France

ISBN: (纸本)9781509014378

Human action recognition has emerged as one of the most challenging and active areas of research in the computer vision domain. In addition to pose variation and scale variability, high complexity of human motions and the variability of object interactions represent additional significant challenges. In this paper, we present an approach for human-object interaction modeling and classification. Towards that goal, we adopt relevant frame-level features;the inter-joint distances and joints-object distances. These proposed features are efficiently insensitive to position and pose variation. The evolution of the these distances in time is modeled by trajectories in a high dimension space and a shape analysis framework is used to model and compare the trajectories corresponding to human-object interaction in a Riemannian manifold. The experiments conducted following state-of-the-art settings and results demonstrate the strength of the proposed method. Using only the skeletal information, we achieve state-of-the-art classification results on the benchmark dataset.

关键词： Trajectories

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