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检索条件"任意字段=2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005"

共 6545 条记录，以下是3911-3920 订阅

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Evolving Effective Color Features for Improving FRGC Baseline Performance

Evolving Effective Color Features for Improving FRGC Baselin...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： Peichung Shih Chengjun Liu Department of Computer Science New Jersey Institute of Technology Newark NJ USA

This paper presents a novel color feature extraction method for face recognition. Firstly, a new color space, LC1C2, consisting of one luminance (L) channel and two chrominance channels (C1,C2) is introduced as a linear transformation of the input RGB color space. The specific transformation from the RGB color space to the LC1C2 color space is then optimized by Genetic Algorithms (GAs) where a fitness function guides the evolution toward higher recognition accuracy. The feasibility of our feature extraction method has been successfully demonstrated using Face recognition Grand Challenge (FRGC) databases and the Biometric Experimentation Environment (BEE) baseline algorithm. Specifically, when experimenting with the FRGC version 1 experiment #4, the extracted color features achieve 75% and 73% rank-one face recognition rates using the Principal Component Analysis (PCA) and the Fisher Linear Discriminant (FLD) methods, respectively. When using the FRGC version 2 experiment #4, the extracted color features improve the face verification rate (at 0.1% false acceptance rate) of the BEE baseline algorithm from 12% to 32% and 55% using PCA and FLD, respectively.

关键词： Feature extraction Face recognition Principal component analysis Image databases Face detection Space technology Genetic algorithms Skin computer science Biometrics

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Performance Evaluation of Face recognition using Visual and Thermal Imagery with Advanced Correlation Filters

Performance Evaluation of Face Recognition using Visual and ...

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ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： Jingu Heo M. Savvides B.V.K. Vijayakumar Department of Electrical and Computer Engineering Carnegie Mellon University USA

This paper presents the face recognition performance evaluation using visual and thermal infrared (IR) face images with correlation filter methods. New correlation filter designs have shown to be distortion invariant and the advantages of using thermal IR images are due to their invariance to visible illumination variations. A combined use of thermal IR image data and correlation filters makes a viable means for improving the performance of face recognition techniques, especially beyond visual spectrum. Subset of Equinox databases are used for the performance evaluation. Among various advanced correlation filters, minimum average correlation energy (MACE) filters and optimum trade-off synthetic discriminant function (OTSDF) filters are used in our experiments. We show that correlation filters perform well when the size of face is of significantly low resolution (e.g. 20x20 pixels). Performing robust face recognition using low resolution images has many applications including performing human identification at a distance (HID). The eyeglass detection and removal in thermal images are processed to increase the performance in thermal face recognition. We show that we can outperform commercial face recognition algorithms such as FaceIt® based on Local Feature Analysis (LFA).

关键词： Face recognition Filters Face detection Energy resolution Infrared imaging Lighting Image databases Visual databases Robustness Image resolution

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Pixels that sound

Pixels that sound

引用

conference on computer vision and pattern recognition (cvpr)

作者： E. Kidron Y.Y. Schechner M. Elad Department Electrical Engineering Technion-Israel Institute of Technology Haifa Israel Department Computer Science Technion-Israel Institute of Technology Haifa Israel

People and animals fuse auditory and visual information to obtain robust perception. A particular benefit of such cross-modal analysis is the ability to localize visual events associated with sound sources. We aim to achieve this using computer-vision aided by a single microphone. Past efforts encountered problems stemming from the huge gap between the dimensions involved and the available data. This has led to solutions suffering from low spatio-temporal resolutions. We present a rigorous analysis of the fundamental problems associated with this task. Then, we present a stable and robust algorithm which overcomes past deficiencies. It grasps dynamic audio-visual events with high spatial resolution, and derives a unique solution. The algorithm effectively detects pixels that are associated with the sound, while filtering out other dynamic pixels. It is based on canonical correlation analysis (CCA), where we remove inherent ill-posedness by exploiting the typical spatial sparsity of audio-visual events. The algorithm is simple and efficient thanks to its reliance on linear programming and is free of user-defined parameters. To quantitatively assess the performance, we devise a localization criterion. The algorithm capabilities were demonstrated in experiments, where it overcame substantial visual distractions and audio noise.

关键词： Microphones Spatial resolution Motion analysis Robustness Filtering computer vision Auditory system computer science Animals Fuses

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8D-THERMO CAM: combination of geometry with physiological information for face recognition

8D-THERMO CAM: combination of geometry with physiological in...

引用

conference on computer vision and pattern recognition (cvpr)

作者： I.A. Kakadiaris G. Passalis T. Theoharis G. Toderici I. Konstantinidis N. Murtuza Visual Computing Lab Department of Computer Science University of Houston Clear Lake Houston TX USA

Biometrics-based technologies in the area of identity management are gaining increasing importance, as a means of establishing non-falsifiable credentials for end users. However, in the three-way tug-of-war between convenient, unobtrusive data collection (required for user acceptance), accuracy in results (required for justifying deployment), and speed (required for widespread use in practice), no single biometric to date has managed to hold the middle ground that would allow for its ready adoption. The overall goal of our project is to develop the theoretical framework and computational tools that will lead to the development of a practical, unobtrusive, and accurate face recognition system for convenient and effective access control. This framework encompasses 8D characteristics of the face (3D geometry+2D visible texture+2D infrared texture, over time). In this paper, we present a novel multi-modal facial recognition approach that employs data from both visible spectrum and thermal infrared sensors. From the fitted parametric model we extract two images corresponding to the subject's face and process these images to extract biometric signatures. Specifically, the deformation image is compressed using a wavelet transform and the vasculature graph is extracted from the parametric thermal image.

关键词： computer aided manufacturing CADCAM Information geometry Face recognition Data mining Biometrics Identity management systems Technology management Access control Infrared sensors

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Jensen-Shannon boosting learning for object recognition

Jensen-Shannon boosting learning for object recognition

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conference on computer vision and pattern recognition (cvpr)

作者： Xiangsheng Huang S.Z. Li Yangsheng Wang CASIA-SAIT HCI Joint Lab Institute of Automation Chinese Academy and Sciences Beijing China Institute of Automation Chinese Academy and Sciences Beijing China

In this paper, we propose a novel learning method, called Jensen-Shannon Boosting (JSBoost) and demonstrate its application to object recognition. JSBoost incorporates Jensen-Shannon (JS) divergence [Y. Bubner et al. (2001)] into AdaBoost learning. JS divergence is advantageous in that it provides more appropriate measure of dissimilarity between two classes and it is numerically more stable than other measures such as Kullback-Leibler (KL) divergence (see [Y. Bubner et al. (2001)]). The best features are iteratively learned by maximizing the projected JS divergence, based on which best weak classifiers are derived. The weak classifiers are combined into a strong one by minimizing the recognition error. JSBoost learning is demonstrated with face object recognition using a local binary pattern (LBP) [M. Pietikainen et al. (2004)] based representation. JSBoost selects the best LBP features from thousands of candidate features and constructs a strong classifier based on the selected features. JSBoost empirically produces better face recognition results than other AdaBoost variants such as RealBoost [R.E. Schapire et al. (1998)], GentleBoost [J. Friedman et al. (2000)] and KL-Boost [C. Liu et al. (2003)], as demonstrated by experiments.

关键词： Boosting Object recognition Automation Linear discriminant analysis Human computer interaction Learning systems Face recognition Upper bound pattern recognition Feature extraction

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A measure of deformability of shapes, with applications to human motion analysis

A measure of deformability of shapes, with applications to h...

引用

conference on computer vision and pattern recognition (cvpr)

作者： A.K. Roy-Chowdhury Department of Electrical Engineering University of California Riverside CA USA

In this paper we develop a theory for characterizing how deformable a shape is. We define a term called "deformability index" for shapes. The deformability index is computed from the tracked positions of a sequence of deformable shapes, using a scaled orthographic camera projection model. Our method assumes that a deformable shape sequence can be represented by a linear combination of basis shapes, where the weights assigned to each basis shape changes with time. The tracked points obtained from the shape sequence is transformed to a 3D shape space. Using statistical models to separate out the "true" deformations from those induced by noise in the trajectories, the dimension of this shape space is estimated using spectral analysis methods. The dimension of this shape space determines the number of basis shapes needed to represent the shape sequence, which, in turn, determines the deformability index. Rigid 3D transformations of the shape are taken into account in estimating the deformability index; however, the method does not require estimation of 3D structure or motion. Experimental results are shown using motion capture data as well as real imagery of different human activities. The results show that the deformability index is in accordance with our intuitive judgement and corroborates certain hypotheses in human movement analysis studies.

关键词： Shape measurement Motion measurement Humans Motion analysis Deformable models Spectral analysis Motion estimation computer vision Legged locomotion Cameras

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Real time texture classification using field programmable gate arrays

Real time texture classification using field programmable ga...

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33rd Applied Imagery pattern recognition Workshop

作者： Wall, Geoffrey Iqbal, Faizal Isaacs, Jason Liu, Xiuwen Foo, Simon Center for Applied Vision and Imaging Sciences Florida State University Tallahassee FL 32306 United States Machine Intelligence Lab. FAMU College of Engineering Tallahassee FL 32310 United States

ISBN: (纸本)0769522505

In this paper we present a novel hardware / software approach to implement a highly accurate texture classification algorithm. We propose the use of field programmable gate arrays (FPGAs) to efficiently compute multiple convolutions in parallel that are required by the spectral histogram representation we employ. The combination of custom hardware and software allows us to have a classifier that is able to achieve results of over 99% accuracy at a rate of roughly 6000 image classifications per second on a challenging real texture dataset. © 2005 ieee.

关键词： pattern recognition

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Selection of tuning parameters for support vector machines

Selection of tuning parameters for support vector machines

引用

2005 ieee International conference on Acoustics, Speech, and Signal Processing, ICASSP '05

作者： Solo, Victor Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor Ann Arbor MI 48109 United States

ISBN: (纸本)0780388747

Support Vector machines have become important in classification, biometrics, machine learning and pattern recognition. But successful application requires selection of various tuning parameters such as kernel parameters and penalty or margin parameters. We apply a new technique for this problem which provides very simple structure for the automatic selector. © 2005 ieee.

关键词： Database systems

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Biologically inspired approaches to automated feature extraction and target recognition

Biologically inspired approaches to automated feature extrac...

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33rd Applied Imagery pattern recognition Workshop

作者： Carpenter, Gail A. Martens, Siegfried Mingolla, Ennio Ogas, Ogi J. Sai, Chaitanya Boston University Department of Cognitive and Neural Systems 677 Beacon Street Boston MA 02215 United States

ISBN: (纸本)0769522505

Ongoing research at Boston University has produced computational models of biological vision and learning that embody a growing corpus of scientific data and predictions, vision models perform long-range grouping and figure/ground segmentation, and memory models create attentionally controlled recognition codes that intrinsically combine bottom-up activation and top-down learned expectations. These two streams of research form the foundation of novel dynamically integrated systems for image understanding. Simulations using multi-spectral images illustrate road completion across occlusions in a cluttered scene and information fusion from input labels that are simultaneously inconsistent and correct. The CMS vision and Technology Labs (***/visionlab and ***/techlab) are further integrating science and technology through analysis, testing, and development of cognitive and neural models for large-scale applications, complemented by software specification and code distribution. © 2005 ieee.

关键词： Feature extraction

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Fisher+Kernel criterion for discriminant analysis

Fisher+Kernel criterion for discriminant analysis

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conference on computer vision and pattern recognition (cvpr)

作者： Shu Yang Shuicheng Yan Dong Xu Xiaoou Tang Chao Zhang National Laboratory on Machine Perception First Hospital Peking University Beijing China Department of Information Engineering Chinese University of Hong Kong Sha Tin Hong Kong China MOE-Microsoft Key Laboratory of Multimedia Computing and Communication & Department of EEIS University of Science and Technology Hefei Anhui China Microsoft Research Asia Beijing China

ISBN: (纸本)0769523722

We simultaneously approach two tasks of nonlinear discriminant analysis and kernel selection problem by proposing a unified criterion, Fisher+Kernel criterion. In addition, an efficient procedure is derived to optimize this new criterion in an iterative manner. More specifically, original input vector is first transformed into a higher dimensional feature matrix through a battery of nonlinear mappings involved in different kernels. Then, based on the feature matrices, FKC is presented within two coupled projection spaces: one projection space is used to search for the optimal combinations of kernels; while the other encodes the optimal nonlinear discriminating projection directions. Our proposed method is a unified framework for both kernel selection and nonlinear discriminant analysis. Besides, the algorithm potentially alleviates overfitting problem existing in traditional KDA and has no singularity problems in most cases. The effectiveness of our proposed algorithm is validated by extensive face recognition experiments on several datasets.

关键词： Kernel Laboratories Asia Face recognition Linear discriminant analysis Design optimization Chaotic communication Multimedia computing Batteries Couplings

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