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检索条件"任意字段=26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR"

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Bacteria Foraging Fusion For Face recognition Across Age Progression

Bacteria Foraging Fusion For Face Recognition Across Age Pro...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Yadav, Daksha Vatsa, Mayank Singh, Richa Tistarelli, Massimo IIIT Delhi Delhi India Univ Sassari I-07100 Sassari Italy

ISBN: (纸本)9780769549903

the phenomenon of "aging" in humans leads to significant variations in facial features. Various factors such as bone growth, ethnicity and dietary habits influence the facial aging pattern. this increases the difficulty in performing automated face recognition. In this paper, we propose an algorithm that improves the performance of face recognition by applying the bacteria foraging fusion algorithm. the proposed algorithm mitigates the effect of facial changes caused due to aging by combining the LBP features of global and local facial regions at match score level, by means of the bacteria foraging fusion algorithm. Experimental results are presented using the FG-Net and IIITDelhi face aging databases. the IIITDelhi database, which has been collected by the authors, consists of over 2600 age-separated labeled face images of 102 individuals. To account for real life and natural conditions, images include changes in the face due to illumination, pose, and presence of accessories such as eyeglasses. the results demonstrate that the proposed approach outperforms traditional fusion schemes, existing algorithms and a commercial system.

关键词： age issues face recognition feature extraction image fusion lighting pose estimation

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Detecting and Aligning Faces by Image Retrieval

Detecting and Aligning Faces by Image Retrieval

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Shen, Xiaohui Lin, Zhe Brandt, Jonathan Wu, Ying Northwestern Univ 2145 Sheridan Rd Evanston IL 60208 USA Adobe Res San Jose CA 95110 USA

ISBN: (纸本)9780769549897

Detecting faces in uncontrolled environments continues to be a challenge to traditional face detection methods[24] due to the large variation in facial appearances, as well as occlusion and clutter. In order to overcome these challenges, we present a novel and robust exemplar-based face detector that integrates image retrieval and discriminative learning. A large database of faces with bounding rectangles and facial landmark locations is collected, and simple discriminative classifiers are learned from each of them. A voting-based method is then proposed to let these classifiers cast votes on the test image through an efficient image retrieval technique. As a result, faces can be very efficiently detected by selecting the modes from the voting maps, without resorting to exhaustive sliding window-style scanning. Moreover, due to the exemplar-based framework, our approach can detect faces under challenging conditions without explicitly modeling their variations. Evaluation on two public benchmark datasets shows that our new face detection approach is accurate and efficient, and achieves the state-of-the-art performance. We further propose to use image retrieval for face validation (in order to remove false positives) and for face alignment/landmark localization. the same methodology can also be easily generalized to other face-related tasks, such as attribute recognition, as well as general object detection.

关键词： face recognition image retrieval learning (artificial intelligence) object detection

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Towards Pose Robust Face recognition

Towards Pose Robust Face Recognition

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Yi, Dong Lei, Zhen 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)9780769549897

Most existing pose robust methods are too computational complex to meet practical applications and their performance under unconstrained environments are rarely evaluated. In this paper, we propose a novel method for pose robust face recognition towards practical applications, which is fast, pose robust and can work well under unconstrained environments. Firstly, a 3D deformable model is built and a fast 3D model fitting algorithm is proposed to estimate the pose of face image. Secondly, a group of Gabor filters are transformed according to the pose and shape of face image for feature extraction. Finally, PCA is applied on the pose adaptive Gabor features to remove the redundances and Cosine metric is used to evaluate the similarity. the proposed method has three advantages: (1) the pose correction is applied in the filter space rather than image space, which makes our method less affected by the precision of the 3D model;(2) By combining the holistic pose transformation and local Gabor filtering, the final feature is robust to pose and other negative factors in face recognition;(3) the 3D structure and facial symmetry are successfully used to deal with self-occlusion. Extensive experiments on FERET and PIE show the proposed method outperforms state-of-the-art methods significantly, meanwhile, the method works well on LFW.

关键词： Gabor filters face recognition feature extraction hidden feature removal pose estimation principal component analysis solid modelling

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A semi-automatic methodology for facial landmark annotation

A semi-automatic methodology for facial landmark annotation

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Sagonas, Cliristos Tzimiropoulos, Georgios Zafeiriou, Stefanos Pantic, Maja Imperial Coll London Dept Comp London England

ISBN: (纸本)9780769549903

Developing powerful deformable face models requires massive, annotated face databases on which techniques can be trained, validated and tested. Manual annotation of each facial image in terms of landmarks requires a trained expert and the workload is usually enormous. Fatigue is one of the reasons that in some cases annotations are inaccurate. this is why, the majority of existing facial databases provide annotations for a relatively small subset of the training images. Furthermore, there is hardly any correspondence between the annotated landmarks across different databases. these problems make cross-database experiments almost infeasible. To overcome these difficulties, we propose a semi-automatic annotation methodology for annotating massive face datasets. this is the first attempt to create a tool suitable for annotating massive facial databases. We employed our tool for creating annotations for MultiPIE, XM2VTS, AR, and FRGC Ver. 2 databases. the annotations will be made publicly available from http://***/resources/facial-point-annotations/. Finally, we present experiments which verify the accuracy of produced annotations.

关键词： face recognition image retrieval visual databases

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Incorporating Structural Alternatives and Sharing into Hierarchy for Multiclass Object recognition and Detection

Incorporating Structural Alternatives and Sharing into Hiera...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Wang, Xiaolong Lin, Liang Huang, Lichao Yan, Shuicheng Sun Yat Sen Univ Guangzhou Guangdong Peoples R China Natl Univ Singapore Dept ECE Singapore Singapore

ISBN: (纸本)9780769549897

this paper proposes a reconfigurable model to recognize and detect multiclass (or multiview) objects with large variation in appearance. Compared with well acknowledged hierarchical models, we study two advanced capabilities in hierarchy for object modeling: (i)"switch" variables(i.e. or-nodes) for specifying alternative compositions, and (ii) making local classifiers (i. e. leaf-nodes) shared among different classes. these capabilities enable us to account well for structural variabilities while preserving the model compact. Our model, in the form of an And-Or Graph, comprises four layers: a batch of leaf-nodes with collaborative edges in bottom for localizing object parts;the or-nodes over bottom to activate their children leaf-nodes;the and-nodes to classify objects as a whole;one root-node on the top for switching multiclass classification, which is also an or-node. For model training, we present an EM-type algorithm, namely dynamical structural optimization (DSO), to iteratively determine the structural configuration, (e.g., leaf-node generation associated with their parent or-nodes and shared across other classes), along with optimizing multi-layer parameters. the proposed method is valid on challenging databases, e.g., PASCAL VOC 2007 and UIUC-People, and it achieves state-of-the-arts performance.

关键词： graph theory image classification object detection object recognition optimisation

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Not Everybody's Special: Using Neighbors in Referring Expressions with Uncertain Attributes

Not Everybody's Special: Using Neighbors in Referring Expres...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Sadovnik, Amir Gallagher, Andrew Chen, Tsuhan Cornell Univ Sch Elect & Comp Engn Ithaca NY 14853 USA

ISBN: (纸本)9780769549903

Referring expression generation is widely considered a basic building block of any natural language generation system. Generating these phrases, which can point out a single object from a group of objects, has been studied extensively in that community. However, to build systems which can discuss images in an intelligent way, it is necessary to consider additional factors unique to the visual domain. In this paper we consider the use of neighbors as anchors to create a referring expression for a person in a group image. We describe a target person using the people around him, when we cannot find a reliable set of attributes to describe the target himself. We first present a method for including neighbors in a referring expression, and discuss several ways of presenting this data to a user. We show through experiments that using descriptions with neighbors can significantly improve the probability of conveying the correct information to a user.

关键词： gesture recognition natural language processing probability

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Ground Truth For Pedestrian Analysis and Application to Camera Calibration

Ground Truth For Pedestrian Analysis and Application to Came...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Creusot, Clement Courty, Nicolas Toshiba R&D Ctr Kawasaki Kanagawa Japan UBS IRISA Vannes France

ISBN: (纸本)9780769549903

this paper investigates the use of synthetic 3D scenes to generate ground truth of pedestrian segmentation in 2D crowd video data. Manual segmentation of objects in videos is indeed one of the most time-consuming type of assisted labeling. A big gap in computer vision research can not be filled due to this lack of temporally dense and precise segmentation ground truth on large video samples. Such data is indeed essential to introduce machine learning techniques for automatic pedestrian segmentation, as well as many other applications involving occluded people. We present a new dataset of 1.8 million pedestrian silhouettes presenting human-to-human occlusion patterns likely to be seen in real crowd video data. To our knowledge, it is the first publicly available large dataset of pedestrian in crowd silhouettes. Solutions to generate and represent this data are detailed. We discuss ideas of how this ground truth can be used for a large number of computer vision applications and demonstrate it on a camera calibration toy problem.

关键词： calibration cameras computer vision image segmentation learning (artificial intelligence) video signal processing

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Face recognition Across Poses Using A Single 3D Reference Model

Face Recognition Across Poses Using A Single 3D Reference Mo...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Hsu, Gee-Sern Peng, Hsiao-Chia Natl Taiwan Univ Sci & Technol Taipei 106 Taiwan

ISBN: (纸本)9780769549903

Approaches for cross-pose face recognition can be split into 2D image based and 3D model based. Many 2D based methods are reported with promising performance but can only work for poses same as those in the training set. Although 3D based methods can handle arbitrary poses, only a small number of approaches are available. Extended from a latest face reconstruction method using a single 3D reference model, this study focuses on using the reconstructed 3D face for recognition. the reconstructed 3D face allows the generation of multi-pose samples for recognition. the recognition performance varies with poses, the closer the pose to the frontal, the better the performance attained. Several ways to improve the performance are attempted, including different numbers of fiducial points for alignment, multiple reference models considered in the reconstruction phase, and both frontal and profile poses available in the gallery. these attempts make this approach competitive to the state-of-the-art methods.

关键词： face recognition image reconstruction pose estimation solid modelling

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A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problems

A Comparative Study of Modern Inference Techniques for Discr...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Kappes, Joerg H. Andres, Bjoern Hamprecht, Fred A. Schnoerr, Christoph Nowozin, Sebastian Batra, Dhruv Kim, Sungwoong Kausler, Bernhard X. Lellmann, Jan Komodakis, Nikos Rother, Carsten Heidelberg Univ Bergheimer Str 58 D-69115 Heidelberg Germany Harvard Univ Cambridge MA 02138 USA Microsoft Res Cambridge Cambridge England

ISBN: (纸本)9780769549897

Seven years ago, Szeliski et al. published an influential study on energy minimization methods for Markov random fields (MRF). this study provided valuable insights in choosing the best optimization technique for certain classes of problems. While these insights remain generally useful today, the phenominal success of random field models means that the kinds of inference problems we solve have changed significantly. Specifically, the models today often include higher order interactions, flexible connectivity structures, large label-spaces of different cardinalities, or learned energy tables. To reflect these changes, we provide a modernized and enlarged study. We present an empirical comparison of 24 state-of-art techniques on a corpus of 2,300 energy minimization instances from 20 diverse computer vision applications. To ensure reproducibility, we evaluate all methods in the OpenGM2 framework and report extensive results regarding runtime and solution quality. Key insights from our study agree with the results of Szeliski et al. for the types of models they studied. However, on new and challenging types of models our findings disagree and suggest that polyhedral methods and integer programming solvers are competitive in terms of runtime and solution quality over a large range of model types.

关键词： Markov processes computer vision inference mechanisms integer programming minimisation random processes

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thETIS: three Dimensional Tennis Shots A human action dataset

THETIS: THree Dimensional Tennis Shots A human action datase...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Gourgari, Sofia Goudelis, Georgios Karpouzis, Konstantinos Kollias, Stefanos Natl Tech Univ Athens Image Video & Multimedia Syst Lab GR-10682 Athens Greece

ISBN: (纸本)9780769549903

the detection and classification of human movements, as a joint field of computer vision and pattern recognition, is used with an increasing rate in applications designed to describe human activity. Such applications require efficient methods and tools for the automatic analysis and classification of motion capture data, which constitute an active field of research. To facilitate the development and the benchmarking of methods for action recognition, several video collections have previously been proposed. In this paper, we present a new video database that can be used for an objective comparison and evaluation of different motion analysis and classification methods. the database contains video clips that capture the 3D motion of individuals. To be more specific, the set consists of 8374 video clips, which contain 12 different types of tennis actions performed by 55 individuals, captured by Kinect. Kinect provides the depth map of motion data and helps to extract the 3D skeletal joint connections. Performing experiments using state of the art algorithms, the database shows to be very challenging. It contains very similar to each other actions, offering the opportunity to algorithms dedicated to gaming and athletics, to be developed and tested. the database is freely available for research purposes.

关键词： computer vision feature extraction image classification image motion analysis interactive devices object detection object recognition sport video databases

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