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Fusion of Face and Palmprint for Personal Identification Based on Ordinal Features

Fusion of Face and Palmprint for Personal Identification Bas...

引用

Conference on computer vision and pattern recognition (CVPR)

作者： Rufeng Chu Shengcai Liao Yufei Han Zhenan Sun Stan Z. Li Tieniu Tan Center for Biometrics and Security Research & National Laboratory of Pattern Recognition Institute of Automation Chinese Academy and Sciences China

In this paper, we present a face and palmprint multimodal biometric identification method and system to improve the identification performance. Effective classifiers based on ordinal features are constructed for faces and palmprints, respectively. Then, the matching scores from the two classifiers are combined using several fusion strategies. Experimental results on a middle-scale data set have demonstrated the effectiveness of the proposed system.

关键词： Biometrics Face detection Lighting Sun Information security National security Laboratories pattern recognition Automation Authentication

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Semi-supervised Learning on Semantic Manifold for Event Analysis in Dynamic Scenes

Semi-supervised Learning on Semantic Manifold for Event Anal...

引用

Conference on computer vision and pattern recognition (CVPR)

作者： Lun Xin Tieniu Tan National Laboratory or Pattern Recognition Institute of Automation Chinese Academy and Sciences Beijing China

Events can be considered as obvious changes of important properties with semantic meanings. Usually, all these properties are measurable and continual in complex formats and higher dimensions. It is hard to define and measure semantic events on the original observed data. However, according to the perception process of human being, these spatial-temporal continuous data can be mapped onto corresponding smooth manifolds, and different appearances on manifolds can indicate different semantic meanings. In this paper, we propose a semi-supervised learning method, which is based on partially labeled data, to map original observed data onto semantic manifolds for events definition and analysis in dynamic scenes. Furthermore we also perform semantic representations for various events in real world scenes. Finally, we present experimental results to evaluate the performance of our method.

关键词： Semisupervised learning Layout pattern analysis pattern recognition computer vision Surveillance Data mining Extraterrestrial measurements Laboratories Automation

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A Human Action recognition System for Embedded computer vision Application

A Human Action Recognition System for Embedded Computer Visi...

引用

Conference on computer vision and pattern recognition (CVPR)

作者： Hongying Meng Nick Pears Chris Bailey Department of Computer Science University of York Heslington Yorkshire UK

In this paper, we propose a human action recognition system suitable for embedded computer vision applications in security systems, human-computer interaction and intelligent environments. Our system is suitable for embedded computer vision application based on three reasons. Firstly, the system was based on a linear support vector machine (SVM) classifier where classification progress can be implemented easily and quickly in embedded hardware. Secondly, we use compacted motion features easily obtained from videos. We address the limitations of the well known motion history image (MHI) and propose a new hierarchical motion history histogram (HMHH) feature to represent the motion information. HMHH not only provides rich motion information, but also remains computationally inexpensive. Finally, we combine MHI and HMHH together and extract a low dimension feature vector to be used in the SVM classifiers. Experimental results show that our system achieves significant improvement on the recognition performance.

关键词： Humans computer vision Application software Support vector machines Support vector machine classification History computer security Machine intelligence Hardware Videos

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Deformable Surface Tracking Ambiguities

Deformable Surface Tracking Ambiguities

引用

Conference on computer vision and pattern recognition (CVPR)

作者： Mathieu Salzmann Vincent Lepetit Pascal Fua Computer Vision Laboratory École Polytechnique Fédérale de Lausanne Lausanne Switzerland

We study from a theoretical standpoint the ambiguities that occur when tracking a generic deformable surface under monocular perspective projection given 3D to 2D correspondences. We show that, additionally to the known scale ambiguity, a set of potential ambiguities can be clearly identified. From this, we deduce a minimal set of constraints required to disambiguate the problem and incorporate them into a working algorithm that runs on real noisy data.

关键词： Shape Surface reconstruction Image reconstruction Surface texture Deformable models Linear systems Video sequences Machine learning Plastics computer vision

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A Bayesian Non-Gaussian Mixture Analysis: Application to Eye Modeling

A Bayesian Non-Gaussian Mixture Analysis: Application to Eye...

引用

Conference on computer vision and pattern recognition (CVPR)

作者： Nizar Bouguila Djemel Ziou Riad I. Hammoud CIISE Concordia University Montreal QUE Canada DI Université de Sherbrook Sherbrooke QUE Canada Adv. Control & Security Delphi Electronics and Safety Kokomo IN USA

Many computer vision and pattern recognition problems involve the use of finite Gaussian mixture models. Finite mixture model using generalized Dirichlet distribution has been shown as a robust alternative of normal mixtures. In this paper, we adopt a Bayesian approach for generalized Dirichlet mixture estimation and selection. This approach, offers a solid theoretical framework for combining both the statistical model learning and the knowledge acquisition. The estimation of the parameters is based on the Monte Carlo simulation technique of Gibbs sampling mixed with a Metropolis-Hastings step. For the selection of the number of clusters, we used Bayes factors. We have successfully applied the proposed Bayesian framework to model IR eyes. Experimental results are shown to demonstrate the robustness, efficiency, and accuracy of the algorithm.

关键词： Bayesian methods Robustness Application software computer vision pattern recognition Solid modeling Knowledge acquisition Parameter estimation Sampling methods Eyes

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Fast Keypoint recognition in Ten Lines of Code

Fast Keypoint Recognition in Ten Lines of Code

引用

Conference on computer vision and pattern recognition (CVPR)

作者： Mustafa Ozuysal Pascal Fua Vincent Lepetit Computer Vision Laboratory École Polytechnique Féderal de Lausanne Lausanne Switzerland

While feature point recognition is a key component of modern approaches to object detection, existing approaches require computationally expensive patch preprocessing to handle perspective distortion. In this paper, we show that formulating the problem in a Naive Bayesian classification framework makes such preprocessing unnecessary and produces an algorithm that is simple, efficient, and robust. Furthermore, it scales well to handle large number of classes. To recognize the patches surrounding keypoints, our classifier uses hundreds of simple binary features and models class posterior probabilities. We make the problem computationally tractable by assuming independence between arbitrary sets of features. Even though this is not strictly true, we demonstrate that our classifier nevertheless performs remarkably well on image datasets containing very significant perspective changes.

关键词： Object detection Robustness computer vision Bayesian methods Image recognition Testing Image retrieval Laboratories Image registration Casting

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Online Learning Asymmetric Boosted Classifiers for Object Detection

Online Learning Asymmetric Boosted Classifiers for Object De...

引用

Conference on computer vision and pattern recognition (CVPR)

作者： Minh-Tri Pham Tat-Jen Cham School of Computer Engineering Nanyang Technological University Singapore

We present an integrated framework for learning asymmetric boosted classifiers and online learning to address the problem of online learning asymmetric boosted classifiers, which is applicable to object detection problems. In particular, our method seeks to balance the skewness of the labels presented to the weak classifiers, allowing them to be trained more equally. In online learning, we introduce an extra constraint when propagating the weights of the data points from one weak classifier to another, allowing the algorithm to converge faster. In compared with the Online Boosting algorithm recently applied to object detection problems, we observed about 0-10% increase in accuracy, and about 5-30% gain in learning speed.

关键词： Object detection Face detection Boosting Face recognition Organizing computer vision Target recognition Databases Information retrieval Costs

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Human Activity recognition Based on R Transform

Human Activity Recognition Based on R Transform

引用

Conference on computer vision and pattern recognition (CVPR)

作者： Ying Wang Kaiqi Huang Tieniu Tan National Laboratory of Pattern Recognition Institute of Automation Chinese Academy and Sciences Beijing China

This paper addresses human activity recognition based on a new feature descriptor. For a binary human silhouette, an extended radon transform, R transform, is employed to represent low-level features. The advantage of the R transform lies in its low computational complexity and geometric invariance. Then a set of HMMs based on the extracted features are trained to recognize activities. Compared with other commonly-used feature descriptors, R transform is robust to frame loss in video, disjoint silhouettes and holes in the shape, and thus achieves better performance in recognizing similar activities. Rich experiments have proved the efficiency of the proposed method.

关键词： Humans Shape Discrete transforms pattern recognition Hidden Markov models Feature extraction Robustness Data mining Noise shaping Performance loss

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Online Appearance Model Learning for Video-Based Face recognition

Online Appearance Model Learning for Video-Based Face Recogn...

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Conference on computer vision and pattern recognition (CVPR)

作者： Liang Liu Yunhong Wang Tieniu Tan National Laboratory of Pattern Recognition Institute of Automation Chinese Academy and Sciences Beijing China School of Computer Science and Engineering Beihang University Beijing China

In this paper, we propose a novel online learning method which can learn appearance models incrementally from a given video stream. The data of each frame in the video can be discarded as soon as it has been processed. We only need to maintain a few linear eigenspace models and a transition matrix to approximately construct face appearance manifolds. It is convenient to use these learnt models for video-based face recognition. There are mainly two contributions in this paper. First, we propose an algorithm which can learn appearance models online without using a pre-trained model. Second, we propose a method for eigenspace splitting to prevent that most samples cluster into the same eigenspace. This is useful for clustering and classification. Experimental results show that the proposed method can both learn appearance models online and achieve high recognition rate.

关键词： Face recognition Streaming media Computational complexity Clustering algorithms Training data Laboratories pattern recognition Automation computer science Maintenance engineering

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Flexible Object Models for Category-Level 3D Object recognition

Flexible Object Models for Category-Level 3D Object Recognit...

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Conference on computer vision and pattern recognition (CVPR)

作者： Akash Kushal Cordelia Schmid Jean Ponce Computer Science Department and Beckman Institute University of Illinois Urbana-Champaign USA Lear Team INRIA Montbonnot France

Today's category-level object recognition systems largely focus on fronto-parallel views of objects with characteristic texture patterns. To overcome these limitations, we propose a novel framework for visual object recognition where object classes are represented by assemblies of partial surface models (PSMs) obeying loose local geometric constraints. The PSMs themselves are formed of dense, locally rigid assemblies of image features. Since our model only enforces local geometric consistency, both at the level of model parts and at the level of individual features within the parts, it is robust to viewpoint changes and intra-class variability. The proposed approach has been implemented, and it outperforms the state-of-the-art algorithms for object detection and localization recently compared in [14] on the Pascal 2005 VOC Challenge Cars Test 1 data.

关键词： Object recognition Solid modeling Layout Assembly Robustness Object detection Testing computer vision pattern matching Shape

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