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Face recognition Using Kernel Ridge Regression

Face Recognition Using Kernel Ridge Regression

引用

Conference on computer vision and pattern recognition (CVPR)

作者： Senjian An Wanquan Liu Svetha Venkatesh Department of Computing Curtin University of Technology Perth WA Australia

In this paper, we present novel ridge regression (RR) and kernel ridge regression (KRR) techniques for multivariate labels and apply the methods to the problem efface recognition. Motivated by the fact that the regular simplex vertices are separate points with highest degree of symmetry, we choose such vertices as the targets for the distinct individuals in recognition and apply RR or KRR to map the training face images into a face subspace where the training images from each individual will locate near their individual targets. We identify the new face image by mapping it into this face subspace and comparing its distance to all individual targets. An efficient cross-validation algorithm is also provided for selecting the regularization and kernel parameters. Experiments were conducted on two face databases and the results demonstrate that the proposed algorithm significantly outperforms the three popular linear face recognition techniques (Eigenfaces, Fisher faces and Laplacian faces) and also performs comparably with the recently developed Orthogonal Laplacian faces with the advantage of computational speed. Experimental results also demonstrate that KRR outperforms RR as expected since KRR can utilize the nonlinear structure of the face images. Although we concentrate on face recognition in this paper, the proposed method is general and may be applied for general multi-category classification problems.

关键词： Face recognition Kernel Linear discriminant analysis Principal component analysis Australia Image recognition Target recognition Image databases Supervised learning computer vision

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Illumination Multiplexing within Fundamental Limits

Illumination Multiplexing within Fundamental Limits

引用

Conference on computer vision and pattern recognition (CVPR)

作者： Netanel Ratner Yoav Y. Schechner Department of Electrical Engineering Technion-Israel Institute of Technology Haifa Israel

Taking a sequence of photographs using multiple illumination sources or settings is central to many computer vision and graphics problems. A growing number of recent methods use multiple sources rather than single point sources in each frame of the sequence. Potential benefits include increased signal-to-noise ratio and accommodation of scene dynamic range. However, existing multiplexing schemes, including Hadamard-based codes, are inhibited by fundamental limits set by Poisson distributed photon noise and by sensor saturation. The prior schemes may actually be counterproductive due to these effects. We derive multiplexing codes that are optimal under these fundamental effects. Thus, the novel codes generalize the prior schemes and have a much broader applicability. Our approach is based on formulating the problem as a constrained optimization. We further suggest an algorithm to solve this optimization problem. The superiority and effectiveness of the method is demonstrated in experiments involving object illumination.

关键词： Lighting computer vision computer graphics Signal to noise ratio Layout Dynamic range Error correction Error correction codes Optoelectronic and photonic sensors Constraint optimization

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Efficiently Determining Silhouette Consistency

Efficiently Determining Silhouette Consistency

引用

Conference on computer vision and pattern recognition (CVPR)

作者： Li Yi David W. Jacobs Institute for Advanced Computer Studies University of Maryland College Park MD USA

Volume intersection is a frequently used technique to solve the Shape-From-Silhouette problem, which constructs a 3D object estimate from a set of silhouettes taken with calibrated cameras. It is natural to develop an efficient algorithm to determine the consistency of a set of silhouettes before performing time-consuming reconstruction, so that inaccurate silhouettes can be omitted. In this paper we first present a fast algorithm to determine the consistency of three silhouettes from known (but arbitrary) viewing directions, assuming the projection is scaled orthographic. The temporal complexity of the algorithm is linear in the number of points of the silhouette boundaries. We further prove that a set of more than three convex silhouettes are consistent if and only if any three of them are consistent. Another possible application of our approach is to determine the miscalibrated cameras in a large camera system. A consistent subset of cameras can be determined on the fly and miscalibrated cameras can also be recalibrated at a coarse scale. Real and synthesized data are used to demonstrate our results.

关键词： Cameras Robot vision systems Testing Jacobian matrices Educational institutions computer vision computer graphics Biometrics Image reconstruction Calibration

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A Unified Probabilistic Framework for Facial Activity Modeling and Understanding

A Unified Probabilistic Framework for Facial Activity Modeli...

引用

Conference on computer vision and pattern recognition (CVPR)

作者： Yan Tong Wenhui Liao Zheng Xue Qiang Ji Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy NY USA

Facial activities are the most natural and powerful means of human communication. Spontaneous facial activity is characterized by rigid head movements, non-rigid facial muscular movements, and their interactions. Current research in facial activity analysis is limited to recognizing rigid or non-rigid motion separately, often ignoring their interactions. Furthermore, although some of them analyze the temporal properties of facial features during facial feature extraction, they often recognize the facial activity statically, ignoring the dynamics of the facial activity. In this paper, we propose to explicitly exploit the prior knowledge about facial activities and systematically combine the prior knowledge with image measurements to achieve an accurate, robust, and consistent facial activity understanding. Specifically, we propose a unified probabilistic framework based on the dynamic Bayesian network (DBN) to simultaneously and coherently represent the rigid and non-rigid facial motions, their interactions, and their image observations, as well as to capture the temporal evolution of the facial activities. Robust computer vision methods are employed to obtain measurements of both rigid and non-rigid facial motions. Finally, facial activity recognition is accomplished through a probabilistic inference by systemically integrating the visual measurements with the facial activity model.

关键词： Face recognition Head Humans Motion analysis Facial features Robustness Bayesian methods computer vision Power system modeling Power engineering computing

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Maximally Stable Colour Regions for recognition and Matching

Maximally Stable Colour Regions for Recognition and Matching

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Conference on computer vision and pattern recognition (CVPR)

作者： Per-Erik Forssen Department of Computer Science University of British Columbia Canada

This paper introduces a novel colour-based affine co-variant region detector. Our algorithm is an extension of the maximally stable extremal region (MSER) to colour. The extension to colour is done by looking at successive time-steps of an agglomerative clustering of image pixels. The selection of time-steps is stabilised against intensity scalings and image blur by modelling the distribution of edge magnitudes. The algorithm contains a novel edge significance measure based on a Poisson image noise model, which we show performs better than the commonly used Euclidean distance. We compare our algorithm to the original MSER detector and a competing colour-based blob feature detector, and show through a repeatability test that our detector performs better. We also extend the state of the art in feature repeatability tests, by using scenes consisting of two planes where one is piecewise transparent. This new test is able to evaluate how stable a feature is against changing backgrounds.

关键词： Detectors Testing Clustering algorithms Image edge detection Performance evaluation Colored noise Pixel Noise measurement Euclidean distance computer vision

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The research of image processing algorithms in AGVS

The research of image processing algorithms in AGVS

引用

International Conference on Wavelet Analysis and pattern recognition (ICWAPR)

作者： Jun-You Zhang Shu-Feng Wang College of Transportation and Vehicle Engineering ShanDong University of Technology Zibo China

Because of its flexibility and reliability, computer vision guidance is applied into automated guided vehicle system on express highway. Taken landmark as guided line symbol, based on road video image acquisition and processing, the guided line can be identified and the input information for guidance control can be obtained. Since common technology of image processing troubles actual application, algorithms are studied emphatically. According to the characteristics of vision guidance on express highway, some algorithms are improved.

关键词： Image processing Road vehicles Image edge detection computer vision Navigation Automated highways Real time systems Laplace equations Wavelet analysis pattern analysis

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Invited Talk;"Visual Intelligence from Video and 3D Sensor Analytics"

Invited Talk;"Visual Intelligence from Video and 3D Sensor A...

引用

Conference on computer vision and pattern recognition (CVPR)

作者： Harpreet S. Sawhney Vision and Learning Technologies Sarnoff Corporation Princeton NJ USA

Video cameras are no longer being used only in their traditional role of providing "viewable pixels", but are rapidly becoming sources of intelligent information about the world. More recently 3D cameras are being developed to directly provide 3D measurements of objects and scenes. Appearance and geometry of objects and scenes, and the temporal dynamics of objects are the key information bearing sources for deriving visual intelligence. This talk will highlight sensor data analysis techniques for creating intelligent representations from 2D and 3D sensors. Intelligent sensor data analytics can be performed for mobile as well as widely distributed static sensor platforms. Applications ranging from 3D video manipulation, 3D situational awareness, wide area surveillance and tracking to video/3D object recognition and fingerprinting will be used to illustrate the work.

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Deriving semantics for image clustering from accumulated user feedbacks

Deriving semantics for image clustering from accumulated use...

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15th ACM International Conference on Multimedia, MM'07

作者： Chen, Yanhua Rege, Manjeet Dong, Ming Fotouhi, Farshad Machine Vision Pattern Recognition Lab. Department of Computer Science Wayne State University Detroit MI 48202 United States Database and Multimedia Systems Group Department of Computer Science Wayne State University Detroit MI 48202 United States

ISBN: (纸本)9781595937025

Image clustering solely based on visual features without any knowledge or background information suffers from the problem of semantic gap. In this paper, we propose SS-NMF: a semi-supervised non-negative matrix factorization framework for image clustering. Accumulated relevance feedback in a CBIR system is treated as user provided supervision for guiding the image clustering. We consider the set of positive images in the feedback as constraints on the clustering specifying that the images "must" be clustered together. Similarly, negative images provide constraints specifying that they "cannot" be clustered along with the positive images. Through an iterative algorithm, we perform symmetric tri-factorization of the image-image similarity matrix to infer the clustering. Theoretically, we prove the correctness of SS-NMF by showing that the algorithm is guaranteed to converge. Through experiments conducted on general purpose image datasets, we demonstrate the superior performance of SS-NMF for clustering images effectively. Copyright 2007 ACM.

关键词： Image analysis

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Trajectory Series Analysis based Event Rule Induction for Visual Surveillance

Trajectory Series Analysis based Event Rule Induction for Vi...

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Conference on computer vision and pattern recognition (CVPR)

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

In this paper, a generic rule induction framework based on trajectory series analysis is proposed to learn the event rules. First the trajectories acquired by a tracking system are mapped into a set of primitive events that represent some basic motion patterns of moving object. Then a minimum description length (MDL) principle based grammar induction algorithm is adopted to infer the meaningful rules from the primitive event series. Compared with previous grammar rule based work on event recognition where the rules are all defined manually, our work aims to learn the event rules automatically. Experiments in a traffic crossroad have demonstrated the effectiveness of our methods. Shown in the experimental results, most of the grammar rules obtained by our algorithm are consistent with the actual traffic events in the crossroad. Furthermore the traffic lights rule in the crossroad can also be leaned correctly with the help of eliminating the irrelevant trajectories.

关键词： Surveillance Hidden Markov models Trajectory Event detection Layout Stochastic processes pattern analysis Prototypes Feeds Uncertainty

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Connecting the Out-of-Sample and Pre-Image Problems in Kernel Methods

Connecting the Out-of-Sample and Pre-Image Problems in Kerne...

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Conference on computer vision and pattern recognition (CVPR)

作者： Pablo Arias Gregory Randall Guillermo Sapiro Universidad de la República Uruguay University of Minnesota USA

Kernel methods have been widely studied in the field of pattern recognition. These methods implicitly map, "the kernel trick," the data into a space which is more appropriate for analysis. Many manifold learning and dimensionality reduction techniques are simply kernel methods for which the mapping is explicitly computed. In such cases, two problems related with the mapping arise: The out-of-sample extension and the pre-image computation. In this paper we propose a new pre-image method based on the Nystrom formulation for the out-of-sample extension, showing the connections between both problems. We also address the importance of normalization in the feature space, which has been ignored by standard pre-image algorithms. As an example, we apply these ideas to the Gaussian kernel, and relate our approach to other popular pre-image methods. Finally, we show the application of these techniques in the study of dynamic shapes.

关键词： Joining processes Kernel Principal component analysis Shape pattern recognition Algorithm design and analysis Image denoising Probability density function Gaussian distribution Data visualization

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