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共 1569 条记录，以下是751-760 订阅

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Towards Multi-view and Partially-occluded Face Alignment 27

Towards Multi-view and Partially-occluded Face Alignment

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Xing, Junliang Niu, Zhiheng Huang, Junshi Hu, Weiming Yan, Shuicheng Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100190 Peoples R China Natl Univ Singapore Dept Elect & Comp Engn Singapore 117576 Singapore NUS Beijing Peoples R China

ISBN: (纸本)9781479951178

We present a robust model to locate facial landmarks under different views and possibly severe occlusions. To build reliable relationships between face appearance and shape with large view variations, we propose to formulate face alignment as an l(1)-induced Stagewise Relational Dictionary (SRD) learning problem. During each training stage, the SRD model learns a relational dictionary to capture consistent relationships between face appearance and shape, which are respectively modeled by the pose-indexed image features and the shape displacements for current estimated landmarks. During testing, the SRD model automatically selects a sparse set of the most related shape displacements for the testing face and uses them to refine its shape iteratively. To locate facial landmarks under occlusions, we further propose to learn an occlusion dictionary to model different kinds of partial face occlusions. By deploying the occlusion dictionary into the SRD model, the alignment performance for occluded faces can be further improved. Our algorithm is simple, effective, and easy to implement. Extensive experiments on two benchmark datasets and two newly built datasets have demonstrated its superior performances over the state-of-the-art methods, especially for faces with large view variations and/or occlusions.

关键词： Face alignment dictionary learning sparse coding

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An Attention-based Activity recognition for Egocentric Video

An Attention-based Activity Recognition for Egocentric Video

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Matsuo, Kenji Yamada, Kentaro Ueno, Satoshi Naito, Sei KDDI R&D Labs Inc Fujimino Saitama Japan

ISBN: (纸本)9781479943098

In this paper, we propose a human activity recognition method from first-person videos, which provides a supplementary method to improve the recognition accuracy. Conventional methods detect objects and derive a user's behavior based on their taxonomy. One of the recent works has achieved accuracy improvement by determining key objects based on hand manipulation. However, such manipulation-based approach has a restriction on applicable scenes and object types because the user's hands don't always present significant information. In contrast, our proposed attention-based approach provides a solution to detect visually salient objects as key objects in a non-contact manner. Experimental results show that the proposed method classifies first-person actions more accurately than the previous method by 6.4 percentage points and its average accuracy reaches 43.3%.

关键词： Object detection

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the Fastest Deformable Part Model for Object Detection 27

The Fastest Deformable Part Model for Object Detection

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Yan, Junjie Lei, Zhen Wen, Longyin Li, Stan Z. Chinese Acad Sci Inst Automat Ctr Biometr & Secur Res Beijing 100864 Peoples R China Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100864 Peoples R China

ISBN: (纸本)9781479951178

this paper solves the speed bottleneck of deformable part model (DPM), while maintaining the accuracy in detection on challenging datasets. three prohibitive steps in cascade version of DPM are accelerated, including 2D correlation between root filter and feature map, cascade part pruning and HOG feature extraction. For 2D correlation, the root filter is constrained to be low rank, so that 2D correlation can be calculated by more efficient linear combination of 1D correlations. A proximal gradient algorithm is adopted to progressively learn the low rank filter in a discriminative manner. For cascade part pruning, neighborhood aware cascade is proposed to capture the dependence in neighborhood regions for aggressive pruning. Instead of explicit computation of part scores, hypotheses can be pruned by scores of neighborhoods under the first order approximation. For HOG feature extraction, look-up tables are constructed to replace expensive calculations of orientation partition and magnitude with simpler matrix index operations. Extensive experiments show that (a) the proposed method is 4 times faster than the current fastest DPM method with similar accuracy on Pascal VOC, (b) the proposed method achieves state-of-the-art accuracy on pedestrian and face detection task with frame-rate speed.

关键词： deformable part model face detection object detection Object detection Facial recognition digital power management Deformable models cascades Neighborhood tree surgery

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Learning Expressionlets on Spatio-Temporal Manifold for Dynamic Facial Expression recognition 27

Learning Expressionlets on Spatio-Temporal Manifold for Dyna...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Liu, Mengyi Shan, Shiguang Wang, Ruiping Chen, Xilin Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Univ Oulu Dept Comp Sci & Engn SF-90100 Oulu Finland

ISBN: (纸本)9781479951178

Facial expression is temporally dynamic event which can be decomposed into a set of muscle motions occurring in different facial regions over various time intervals. For dynamic expression recognition, two key issues, temporal alignment and semantics-aware dynamic representation, must be taken into account. In this paper, we attempt to solve both problems via manifold modeling of videos based on a novel mid-level representation, i.e. expressionlet. Specifically, our method contains three key components: 1) each expression video clip is modeled as a spatio-temporal manifold (STM) formed by dense low-level features;2) a Universal Manifold Model (UMM) is learned over all low-level features and represented as a set of local ST modes to statistically unify all the STMs. 3) the local modes on each STM can be instantiated by fitting to UMM, and the corresponding expressionlet is constructed by modeling the variations in each local ST mode. With above strategy, expression videos are naturally aligned both spatially and temporally. To enhance the discriminative power, the expressionlet-based STM representation is further processed with discriminant embedding. Our method is evaluated on four public expression databases, CK+, MMI, Oulu-CASIA, and AFEW. In all cases, our method reports results better than the known state-of-the-art.

关键词： Semantics

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Recognizing RGB Images by Learning from RGB-D Data 27

Recognizing RGB Images by Learning from RGB-D Data

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Chen, Lin Li, Wen Xu, Dong Agcy Sci Technol & Res Inst Infocomm Res Singapore Singapore Nanyang Technol Univ Sch Comp Engn Singapore Singapore

ISBN: (纸本)9781479951178

In this work, we propose a new framework for recognizing RGB images captured by the conventional cameras by leveraging a set of labeled RGB-D data, in which the depth features can be additionally extracted from the depth images. We formulate this task as a new unsupervised domain adaptation (UDA) problem, in which we aim to take advantage of the additional depth features in the source domain and also cope with the data distribution mismatch between the source and target domains. To effectively utilize the additional depth features, we seek two optimal projection matrices to map the samples from both domains into a common space by preserving as much as possible the correlations between the visual features and depth features. To effectively employ the training samples from the source domain for learning the target classifier, we reduce the data distribution mismatch by minimizing the Maximum Mean Discrepancy (MMD) criterion, which compares the data distributions for each type of feature in the common space. Based on the above two motivations, we propose a new SVM based objective function to simultaneously learn the two projection matrices and the optimal target classifier in order to well separate the source samples from different classes when using each type of feature in the common space. An efficient alternating optimization algorithm is developed to solve our new objective function. Comprehensive experiments for object recognition and gender recognition demonstrate the effectiveness of our proposed approach for recognizing RGB images by learning from RGB-D data.

关键词： RGB-D domain adaptation gender recognition object recognition transfer learning object recognition Transfer (Psychology) data distribution red-green-blue colour model images Objective function Learning

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Grassmann Averages for Scalable Robust PCA 27

Grassmann Averages for Scalable Robust PCA

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Hauberg, Soren Feragen, Aasa Black, Michael J. DTU Compute Lyngby Denmark DIKU Copenhagen Denmark MPIs Tubingen Tubingen Germany MPI Intelligent Syst Tubingen Germany MPI Intelligent Syst Perceiving Syst Dept Tubingen Germany Univ Copenhagen DIKU DK-1168 Copenhagen Denmark MPI Intelligent Syst Machine Learning & Computat Biol Grp Tubingen Germany MPI Dev Biol Tubingen Germany

ISBN: (纸本)9781479951178

As the collection of large datasets becomes increasingly automated, the occurrence of outliers will increase - "big data" implies "big outliers". While principal component analysis (PCA) is often used to reduce the size of data, and scalable solutions exist, it is well-known that outliers can arbitrarily corrupt the results. Unfortunately, state-of-the-art approaches for robust PCA do not scale beyond small-to-medium sized datasets. To address this, we introduce the Grassmann Average (GA), which expresses dimensionality reduction as an average of the subspaces spanned by the data. Because averages can be efficiently computed, we immediately gain scalability. GA is inherently more robust than PCA, but we show that they coincide for Gaussian data. We exploit that averages can be made robust to formulate the Robust Grassmann Average (RGA) as a form of robust PCA. Robustness can be with respect to vectors (subspaces) or elements of vectors;we focus on the latter and use a trimmed average. the resulting Trimmed Grassmann Average (TGA) is particularly appropriate for computer vision because it is robust to pixel outliers. the algorithm has low computational complexity and minimal memory requirements, making it scalable to "big noisy data." We demonstrate TGA for background modeling, video restoration, and shadow removal. We show scalability by performing robust PCA on the entire Star Wars IV movie.

关键词： Grassmann manifold PCA Robust PCA computer vision directional statistics machine learning subspace estimation

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Context-Aware Active Authentication Using Smartphone Accelerometer Measurements

Context-Aware Active Authentication Using Smartphone Acceler...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Primo, Abena Phoha, Vir V. Kumar, Rajesh Serwadda, Abdul Louisiana Tech Univ Ctr Secure Cyberspace Ruston LA 71270 USA

ISBN: (纸本)9781479943098

While body movement patterns recorded by a smartphone accelerometer are now well understood to be discriminative enough to separate users, little work has been done to address the question of if or how the position in which the phone is held affects user authentication. In this work, we show through a combination of supervised learning methods and statistical tests, that there are certain users for whom exploitation of information of how a phone is held drastically improves classification performance. We propose a two-stage authentication framework that identifies the location of the phone before performing authentication, and show its benefits based on a dataset of 30 users. Our work represents a first step towards bridging the gap between accelerometer-based authentication systems analyzed from the context of a laboratory environment and a real accelerometer-based authentication system in the wild where phone positioning cannot be assumed.

关键词： gait recognition context awareness accelerometers authentication

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Robust Low-Rank Regularized Regression for Face recognition with Occlusion

Robust Low-Rank Regularized Regression for Face Recognition ...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Qian, Jianjun Yang, Jian Zhang, Fanlong Lin, Zhouchen Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Peoples R China Peking Univ Key Lab Machine Percept MOE Beijing Peoples R China

ISBN: (纸本)9781479943098

Recently, regression analysis based classification methods are popular for robust face recognition. these methods use a pixel-based error model, which assumes that errors of pixels are independent. this assumption does not hold in the case of contiguous occlusion, where the errors are spatially correlated. Observing that occlusion in a face image generally leads to a low-rank error image, we propose a low-rank regularized regression model and use the alternating direction method of multipliers (ADMM) to solve it. We thus introduce a novel robust low-rank regularized regression (RLR3) method for face recognition with occlusion. Compared with the existing structured sparse error coding models, which perform error detection and error support separately, our method integrates error detection and error support into one regression model. Experiments on benchmark face databases demonstrate the effectiveness and robustness of our method, which outperforms state-of-the-art methods.

关键词： Face recognition

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FPGA-based fast response image analysis for autonomous or semi-autonomous indoor flight

FPGA-based fast response image analysis for autonomous or se...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Ladig, Robert Shimonomura, Kazuhiro Ritsumeikan Univ Dept Robot Kusatsu Shiga 5258577 Japan

ISBN: (纸本)9781479943098

Small aerial vehicles, like quadrotor, have a high potential to be helpful tools in first response scenarios like earthquakes, landslides and fires. But even simple tasks like holding position and altitude can be challenging to accomplish by a human operator and even more challenging autonomously. When outdoors, using GPS and pressure sensors is feasible, but indoors or in GPS denied environments it is not. Until now, for indoor flight scenarios either a lot of energy consuming sensors and hardware or a perfectly defined surrounding is required. In this approach, the viability of an onboard FPGA based indoor flight navigation system with a pan-tilt camera mount and a single VGA camera is tested. It can be used to either support an operator performing a hold position and altitude task, or act completely autonomously to achieve this task.

关键词： Aerial robotics FPGA Monocular vision Onboard vision Quadrotor Monocular vision Field programmable gate arrays flight Global Positioning Systems Pressure Sensor Device Component FAST-RESPONSE INDOORS Outdoors

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Real-time Mobile Facial Expression recognition System - A Case Study

Real-time Mobile Facial Expression Recognition System - A Ca...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Suk, Myunghoon Prabhakaran, Balakrishnan Univ Texas Dallas Dept Comp Engn Richardson TX 75080 USA

ISBN: (纸本)9781479943098

this paper presents a mobile application for real time facial expression recognition running on a smart phone with a camera. the proposed system uses a set of Support Vector Machines (SVMs) for classifying 6 basic emotions and neutral expression along with checking mouth status. the facial expression features for emotion recognition are extracted by Active Shape Model (ASM) fitting landmarks on a face and then dynamic features are generated by the displacement between neutral and expression features. We show experimental results with 86% of accuracy with 10 folds cross validation in 309 video samples of the extended Cohn-Kanade (CK+) dataset. Using the same SVM models, the mobile app is running on Samsung Galaxy S3 with 2.4 fps. the accuracy of real-time mobile emotion recognition is about 72% for 6 posed basic emotions and neutral expression by 7 subjects who are not professional actors.

关键词： active shape model emotion recognition facial expression recognition mobile vision support vector machine

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