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Kernel sparse representation with local patterns for face recognition

Kernel sparse representation with local patterns for face re...

引用

IEEE International Conference on Image Processing

作者： Cuicui Kang Shengcai Liao Shiming Xiang Chunhong Pan National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences China Department of Computer Science and Engineering Michigan State University USA

In this paper we propose a novel kernel sparse representation classification (SRC) framework and utilize the local binary pattern (LBP) descriptor in this framework for robust face recognition. First we develop a kernel coordinate descent (KCD) algorithm for 11 minimization in the kernel space, which is based on the covariance update technique. Then we extract LBP descriptors from each image and apply two types of kernels (χ 2 distance based and Hamming distance based) with the proposed KCD algorithm under the SRC framework for face recognition. Experiments on both the Extended Yale B and the PIE face databases show that the proposed method is more robust against noise, occlusion, and illumination variations, even with small number of training samples.

关键词： Kernel Face recognition Noise Training Lighting Databases Histograms

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Preliminary Design of a Terrain recognition System

Preliminary Design of a Terrain Recognition System

引用

Annual International Conference of the IEEE engineering in Medicine and Biology Society

作者： Fan Zhang Zheng Fang Ming Liu He Huang department of Electrical Computer and Biomedical Engineering University of Rhode Island National Laboratory for Multispectral Information Processing Technologies Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology

ISBN: (纸本)9781424441211

This paper aims to design a wearable terrain recognition system, which might assist the control of powered artificial prosthetic legs. A laser distance sensor and inertial measurement unit (IMU) sensors were mounted on human body. These sensors were used to identify the movement state of the user, reconstruct the geometry of the terrain in front of the user while walking, and recognize the type of terrain before the user stepped on it. Different sensor configurations were investigated and compared. The designed system was evaluated on one healthy human subject when walking on an obstacle course in the laboratory environment. The results showed that the reconstructed terrain height demonstrated clearer pattern difference among studied terrains when the laser was placed on the waist than that when the laser was mounted on the shank. The designed system with the laser on the waist accurately recognized 157 out of 160 tested terrain transitions, 300ms-2870ms before the user switched the negotiated terrains. These promising results demonstrated the potential application of the designed terrain recognition system to further improve the control of powered artificial legs.

关键词： terrains preliminary design landscapes recognition system Laser design system Consumers Waist range sensor laboratory environment human subject walking

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Random subspace method in text categorization

Random subspace method in text categorization

引用

International Conference on pattern recognition

作者： Gangeh, Mehrdad J. Kamel, Mohamed S. Duin, Robert P. W. Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada Pattern Recognition Laboratory Delft University of Technology Delft Netherlands

ISBN: (纸本)9780769541099

In text categorization (TC), which is a supervised technique, a feature vector of terms or phrases is usually used to represent the documents. Due to the huge number of terms in even a moderate-size text corpus, high dimensional feature space is an intrinsic problem in TC. Random subspace method (RSM), a technique that divides the feature space to smaller ones each submitted to a (base) classifier (BC) in an ensemble, can be an effective approach to reduce the dimensionality of the feature space. Inspired by a similar research on functional magnetic resonance imaging (fMRI) of brain, here we address the estimation of ensemble parameters, i.e., the ensemble size (L) and the dimensionality of feature subsets (M) by defining three criteria: usability, coverage, and diversity of the ensemble. We will show that relatively medium M and small L yield an ensemble that improves the performance of a single support vector machine, which is considered as the state-of-the-art in TC. © 2010 IEEE.

关键词： Support vector machines

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The semantics of personalised web accessibility assessment 10

The semantics of personalised web accessibility assessment

引用

25th Annual ACM Symposium on Applied Computing, SAC 2010

作者： Lopes, Rui Votis, Konstantinos Carriço, Luís Tzovaras, Dimitrios Spiridon, L. LaSIGE/University of Lisbon Edíficio C6 Campo Grande 1749-016 Lisboa Portugal CERTH/ITI Thermi-Thessaloniki GR-57001 Greece Pattern Recognition Laboratory Computer Engineering and Informatics University of Patras Greece

ISBN: (纸本)9781605586380

This paper presents SAAF, a Semantic Accessibility Assessment Framework for personalised Web accessibility assessment procedures. We define four domains as a way to decouple general accessibility concepts and semantics (e.g., disabilities, device descriptions) from technology or assessment specific concepts. We leverage personalised Web accessibility by transforming questions such as "is this Web site accessible to me?" into rich-semantics queries on SAAF and integrate it into a Web accessibility evaluator. © 2010 ACM.

关键词： Semantics

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Introducing accessibility in the Web services domain

Introducing accessibility in the Web services domain

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International Conference on computer Science and Information Technology (CSIT)

作者： Dimitris Giakoumis Konstantinos Votis Dimitrios Tzovaras Spiridon Likothanassis George Hassapis Informatics and Telematics Institute Thessaloniki Greece Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Patras Greece Pattern Recognition Laboratory Computer Engineering and Informatics University of Patras Patras Greece

The ever increasing diversification of Web services and software applications poses a real challenge to developers and designers when creating software that has to cope with a myriad of interaction situations, as well as specific directives for ensuring an accessible interaction. Utilizing an advanced web services accessibility assessment tool, they can obtain a better understanding of the accessibility constraints for people with disabilities within Web services and software application's user interfaces. The proposed Web services assessment tool will assist them, with a minimal effort, to explore user-centered design and important accessibility issues for their software implementations. In an effort to solve such issues, this paper takes a step forward and introduces the notion of accessibility in the web service domain, in order to enhance web services with accessibility features capable to ensure that HCI through applications utilizing them is accessible.

关键词： Variable speed drives Guidelines Software

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Local and Global Regressive Mapping for manifold learning with out-of-sample extrapolation

Local and Global Regressive Mapping for manifold learning wi...

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作者： Yang, Yi Nie, Feiping Xiang, Shiming Zhuang, Yueting Wang, Wenhua College of Computer Science Zhejiang University Hangzhou 310027 China Department of Computer Science and Engineering University of Texas Arlington 76019 United States National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9781577354642

Over the past few years, a large family of manifold learning algorithms have been proposed, and applied to various applications. While designing new manifold learning algorithms has attracted much research attention, fewer research efforts have been focused on out-of-sample extrapolation of learned manifold. In this paper, we propose a novel algorithm of manifold learning. The proposed algorithm, namely Local and Global Regressive Mapping (LGRM), employs local regression models to grasp the manifold structure. We additionally impose a global regression term as regularization to learn a model for out-of-sample data extrapolation. Based on the algorithm, we propose a new manifold learning framework. Our framework can be applied to any manifold learning algorithms to simultaneously learn the low dimensional embedding of the training data and a model which provides explicit mapping of the out-of-sample data to the learned manifold. Experiments demonstrate that the proposed framework uncover the manifold structure precisely and can be freely applied to unseen data. Copyright © 2010, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Mapping

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Random Subspace Method in Text Categorization

Random Subspace Method in Text Categorization

引用

International Conference on pattern recognition

作者： Mehrdad J. Gangeh Mohamed S. Kamel Robert P.W. Duin Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada Pattern Recognition Laboratory Delft University of Technnology Delft Netherlands

ISBN: (纸本)9781424475421

关键词： Support vector machines Text categorization Accuracy Kernel Brain Presses Machine learning

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Introducing Accessibility in the Web Services domain

Introducing Accessibility in the Web Services domain

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2010 3rd IEEE International Conference on computer Science and Information Technology—ICCSIT 2010

作者： Dimitris Giakoumis Konstantinos Votis Dimitrios Tzovaras Spiridon Likothanassis George Hassapis Informatics and Telematics Institute 6th km Charilaou-Thermi Road Thessaloniki GR-57001 Greece Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Pattern Recognition Laboratory Computer Engineering and Informatics University of Patras Rio Patras Greece

关键词： Human computer Interaction, Web services, accessibility ,assessment, software design, User-centered design

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Two-stage sparse representation for robust recognition on large-scale database

Two-stage sparse representation for robust recognition on la...

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作者： He, Ran Hu, Bao Gang Zheng, Wei-Shi Guo, Yan Qing School of Electronic and Information Engineering Dalian University of Technology Dalian China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China Department of Computer Science Queen Mary University of London London United Kingdom

ISBN: (纸本)9781577354642

This paper proposes a novel robust sparse representation method, called the two-stage sparse representation (TSR), for robust recognition on a large-scale database. Based on the divide and conquer strategy, TSR divides the procedure of robust recognition into outlier detection stage and recognition stage. In the first stage, a weighted linear regression is used to learn a metric in which noise and outliers in image pixels are detected. In the second stage, based on the learnt metric, the large-scale dataset is firstly filtered into a small set according to the nearest neighbor criterion. Then a sparse representation is computed by the non-negative least squares technique. The sparse solution is unique and can be optimized efficiently. The extensive numerical experiments on several public databases demonstrate that the proposed TSR approach generally obtains better classification accuracy than the state-of-the-art Sparse Representation Classification (SRC). At the same time, by using the TSR, a significant reduction of computational cost is reached by over fifty times in comparison with the SRC, which enables the TSR to be deployed more suitably for large-scale dataset. Copyright © 2010, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Classification (of information)

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On the Fusion of Periocular and Iris Biometrics in Non-ideal Imagery

On the Fusion of Periocular and Iris Biometrics in Non-ideal...

引用

International Conference on pattern recognition

作者： Damon L. Woodard Shrinivas Pundlik Philip Miller Raghavender Jillela Arun Ross Biometrics and Pattern Recognition Laboratory School of Computing Clemson University USA Lane Department of Computer Science and Electrical Engineering West Virginia University USA

Human recognition based on the iris biometric is severely impacted when encountering non-ideal images of the eye characterized by occluded irises, motion and spatial blur, poor contrast, and illumination artifacts. This paper discusses the use of the periocular region surrounding the iris, along with the iris texture patterns, in order to improve the overall recognition performance in such images. Periocular texture is extracted from a small, fixed region of the skin surrounding the eye. Experiments on the images extracted from the Near Infra-Red (NIR) face videos of the Multi Biometric Grand Challenge (MBGC) dataset demonstrate that valuable information is contained in the periocular region and it can be fused with the iris texture to improve the overall identification accuracy in non-ideal situations.

关键词： Iris recognition Videos Pixel Skin Databases Face recognition

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