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检索条件"任意字段=26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013"

共 656 条记录，以下是221-230 订阅

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Single-Sample Face recognition with Image Corruption and Misalignment via Sparse Illumination Transfer

Single-Sample Face Recognition with Image Corruption and Mis...

引用

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

作者： Zhuang, Liansheng Yang, Allen Y. Zhou, Zihan Sastry, S. Shankar Ma, Yi Univ Sci & Technol China Hefei 230026 Peoples R China Univ Calif Berkeley Dept EECS Berkeley CA 94720 USA Univ Illinois Dept ECE Chicago IL 60680 USA

ISBN: (纸本)9780769549897

Single-sample face recognition is one of the most challenging problems in face recognition. We propose a novel face recognition algorithm to address this problem based on a sparse representation based classification (SRC) framework. the new algorithm is robust to image misalignment and pixel corruption, and is able to reduce required training images to one sample per class. To compensate the missing illumination information typically provided by multiple training images, a sparse illumination transfer (SIT) technique is introduced. the SIT algorithms seek additional illumination examples of face images from one or more additional subject classes, and form an illumination dictionary. By enforcing a sparse representation of the query image, the method can recover and transfer the pose and illumination information from the alignment stage to the recognition stage. Our extensive experiments have demonstrated that the new algorithms significantly outperform the existing algorithms in the single-sample regime and with less restrictions. In particular, the face alignment accuracy is comparable to that of the well-known Deformable SRC algorithm using multiple training images;and the face recognition accuracy exceeds those of the SRC and Extended SRC algorithms using hand labeled alignment initialization.

关键词： computer vision face recognition image classification image representation image resolution image retrieval lighting pose estimation

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Cardiff Conversation Database (CCDb): A Database of Natural Dyadic Conversations

Cardiff Conversation Database (CCDb): A Database of Natural ...

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

作者： Aubrey, Andrew J. Marshall, David Rosin, Paul L. Vandeventer, Jason Cunningham, Douglas W. Wallraven, Christian Cardiff Univ Sch Comp Sci Cardiff CF10 3AX S Glam Wales Brandenburg Tech Univ Cottbus Cottbus Germany Korea Univ Seoul South Korea

ISBN: (纸本)9780769549903

We present the Cardiff Conversation Database (CCDb), a unique 2D audiovisual database containing natural conversations between pairs of people. the database currently contains 30 conversations. To date, eight conversations are fully annotated for speaker activity, facial expressions, head motion, and non-verbal utterances. In this paper we describe the data collection and annotation process. We also provide results of baseline experiments in which an SVM classifier was used to identify which parts of the recordings are from the frontchannel speaker and which are backchannel signals. We believe this database will make a useful contribution to computer vision, affective computing, and cognitive science communities by providing raw data, features, annotations and baseline comparisons.

关键词： audio databases pattern classification support vector machines visual databases

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Automated X-ray object recognition using an efficient search algorithm in multiple views

Automated X-ray object recognition using an efficient search...

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

作者： Mery, Domingo Riffo, Vladimir Zuccar, Irene Pieringer, Christian Pontificia Univ Catolica Chile Dept Comp Sci Santiago Chile

ISBN: (纸本)9780769549903

In order to reduce the security risk of a commercial aircraft, passengers are not allowed to take certain items in their carry-on baggage. For this reason, human operators are trained to detect prohibited items using a manually controlled baggage screening process. In this paper, we propose the use of an automated method based on multiple Xray views to recognize certain regular objects with highly defined shapes and sizes. the method consists of two steps: 'monocular analysis', to obtain possible detections in each view of a sequence, and 'multiple view analysis', to recognize the objects of interest using matchings in all views. the search for matching candidates is efficiently performed using a lookup table that is computed off-line. In order to illustrate the effectiveness of the proposed method, experimental results on recognizing regular objects -clips, springs and razor blades-in pen cases are shown achieving around 93% accuracy for 120 objects. We believe that it would be possible to design an automated aid in a target detection task using the proposed algorithm.

关键词： X-ray imaging object recognition security

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"You're it!": Role Identification using Pairwise Interactions in Tag Games

"You're it!": Role Identification using Pairwise Interaction...

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

作者： Moreno, Alejandro Poppe, Ronald Univ Twente NL-7500 AE Enschede Netherlands

ISBN: (纸本)9780769549903

We aim at designing interactive playgrounds that automatically analyze the behavior of children while playing games, in order to adapt the gameplay and make the games more engaging. In this paper, we focus on recognizing roles in tag games, where children are taggers or runners. We start by tracking the location and motion of individual players, and subsequently recognize pairwise interactions: approach, chase and avoid. At each moment in time, we inspect the full set of pairwise interactions to determine the role of each player. Our approach is fully probabilistic, deals with any number of players and can easily be extended to include other interactions and roles. We evaluate our algorithm using simulations, which show promising results. We intend to extend our framework to recognize variants of the tag game, and to address actual play interactions.

关键词： behavioural sciences computer games interactive systems object recognition object tracking probability

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Constrained Clustering and Its Application to Face Clustering in Videos

Constrained Clustering and Its Application to Face Clusterin...

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

作者： Wu, Baoyuan Zhang, Yifan Hu, Bao-Gang Ji, Qiang CASIA NLPR Beijing 100190 Peoples R China Rensselaer Polytech Inst Troy NY 12180 USA

ISBN: (纸本)9780769549897

In this paper, we focus on face clustering in videos. Given the detected faces from real-world videos, we partition all faces into K disjoint clusters. Different from clustering on a collection of facial images, the faces from videos are organized as face tracks and the frame index of each face is also provided. As a result, many pairwise constraints between faces can be easily obtained from the temporal and spatial knowledge of the face tracks. these constraints can be effectively incorporated into a generative clustering model based on the Hidden Markov Random Fields (HMRFs). Within the HMRF model, the pairwise constraints are augmented by label-level and constraint-level local smoothness to guide the clustering process. the parameters for both the unary and the pairwise potential functions are learned by the simulated field algorithm, and the weights of constraints can be easily adjusted. We further introduce an efficient clustering framework specially for face clustering in videos, considering that faces in adjacent frames of the same face track are very similar. the framework is applicable to other clustering algorithms to significantly reduce the computational cost. Experiments on two face data sets from real-world videos demonstrate the significantly improved performance of our algorithm over state-of-the-art algorithms.

关键词： face recognition hidden Markov models object tracking pattern clustering video signal processing

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the Power is in Your Hands: 3D Analysis of Hand Gestures in Naturalistic Video

The Power is in Your Hands: 3D Analysis of Hand Gestures in ...

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

作者： Ohn-Bar, Eshed Trivedi, Mohan M. Univ Calif San Diego Comp Vis & Robot Res Lab La Jolla CA 92093 USA

ISBN: (纸本)9780769549903

We study natural human activity under difficult settings of cluttered background, volatile illumination, and frequent occlusion. To that end, a two-stage method for hand and hand-object interaction detection is developed. First, activity proposals are generated from multiple sub-regions in the scene. then, these are integrated using a second-stage classifier. We study a set of descriptors for detection and activity recognition in terms of performance and speed. With the overarching goal of reducing 'lab setting bias', a case study is introduced with a publicly available annotated RGB and depth dataset. the dataset was captured using a Kinect under real-world driving settings. the approach is motivated by studying actions-as well as semantic elements in the scene and the driver's interaction with them-which may be used to infer driver inattentiveness. the proposed framework significantly outperforms a state-of-the-art baseline on our dataset for hand detection.

关键词： gesture recognition image classification interactive devices object detection video signal processing

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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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An Augmented Linear Discriminant Analysis Approach for Identifying Identical Twins with the Aid of Facial Asymmetry Features

An Augmented Linear Discriminant Analysis Approach for Ident...

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

作者： Juefei-Xu, Felix Savvides, Marios Carnegie Mellon Univ Dept Elect & Comp Engn CyLab Biometr Ctr Pittsburgh PA 15213 USA

ISBN: (纸本)9780769549903

In this work, we have proposed an Augmented Linear Discriminant Analysis (ALDA) approach to identify identical twins. It learns a common subspace that not only can identify from which family the individual comes, but also can distinguish between individuals within the same family. We evaluate the ALDA against the traditional LDA approach for subspace learning on the Notre Dame twin database. We have shown that the proposed ALDA method with the aid of facial asymmetry features significantly outperforms other well-established facial descriptors (LBP, LTP, LTrP), and the ALDA subspace method does a much better job in distinguishing identical twins than LDA. We are able to achieve 48.50% VR at 0.1% FAR for identifying family membership of identical twin individuals in the crowd and an averaged 82.58% VR at 0.1% FAR for verifying identical twin individuals within the same family, a significant improvement over traditional descriptors and traditional LDA method.

关键词： face recognition feature extraction learning (artificial intelligence)

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

作者： Aoyama, Shoichiro Ito, Koichi Aoki, Takafumi Tohoku Univ Grad Sch Informat Sci Aoba Ku Sendai Miyagi 9808579 Japan

ISBN: (纸本)9780769549903

In the field of biometric recognition, similarity measure using local features such as Gabor-based coding, Local Binary patterns (LBP) and Scale Invariant Feature Transform (SIFT) has been applied to various biometric recognition problems. these features, however, may not always exhibit higher recognition performance than the recognition algorithms of the specific biometric trait. In this paper, we propose a novel similarity measurement technique using local phase features for biometric recognition. the phase information obtained from 2D Discrete Fourier Transform (DFT) of images exhibits good performance for evaluating the similarity between images. the local phase features extracted from multi-scale image pyramids can handle nonlinear deformation of images. through a set of experiments in some biometric recognition such as face, palmprint and finger knuckle recognition, we demonstrate the efficient performance and versatility of the proposed features compared with the state-of-the-art conventional algorithms.

关键词： Gabor filters discrete Fourier transforms face recognition feature extraction palmprint recognition

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Quality Assessment for Fingerprints Collected by Smartphone Cameras

Quality Assessment for Fingerprints Collected by Smartphone ...

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

作者： Li, Guoqiang Yang, Bian Olsen, Martin Aastrup Busch, Christoph Gjovik Univ Coll Norwegian Informat Secur Lab Gjovik Norway

ISBN: (纸本)9780769549903

We propose an approach to assess the quality of fingerprint samples captured by smartphone cameras under real-life scenarios. Our approach extracts a set of quality features for image blocks. Without needing segmentation, the approach determines a sample's quality by checking all image blocks divided from the sample and for each block a trained support vector machine gives a binary indication - "high-quality" or "non-high-quality" (including the low quality case and the background block case). A quality score is then generated for the whole sample. Experiments show this approach performs well in identifying the high quality blocks - the Spearman correlation coefficient between the proposed quality scores and samples' normalized comparison scores (ground truth) reaches 0.53 while the rate of false detection (background blocks judged as high-quality ones) is still low as 4.63 percent over a challenging dataset collected under various real-life scenarios.

关键词： correlation methods feature extraction fingerprint identification image sensors object recognition smart phones support vector machines

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