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Face localization via hierarchical CONDENSATION with Fisher boosting feature selection

Face localization via hierarchical CONDENSATION with Fisher ...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Jilin Tu Zhenqui Zhang Zhihong Zeng T. Huang Electrical and Computer Engineering University of Illinois Urbana-Champaign Urbana-Champaign IL USA

We formulate face localization as a maximum a posteriori probability (MAP) problem of finding the best estimation of human face configuration in a given image. The a prior distribution for intrinsic face configuration is defined by active shape model (ASM). The likelihood model for local facial features is parameterized as mixture of Gaussians in feature space. A hierarchical CONDENSATION framework is then proposed to estimate the face configuration parameter. In order to improve the discriminative power of likelihood distribution in feature space, a new feature subspace, Fisher boosting feature space, is proposed and compared against PCA subspace and biased PCA subspace. Experiments show that, Fisher boosting algorithm can generate strong classifier with less number of weaker classifiers comparing to conventional Adaboosting algorithm as illustrated in a toy problem, that the face localization with Fisher boosting feature subspace outperforms that with PCA feature subspaces in localization accuracy and convergence rate, and that the design of hierarchical CONDENSATION framework alleviates the local minima problem which is frequently encountered by previous ASM optimization algorithms.

关键词： Boosting Principal component analysis Face Humans Active shape model Facial features Gaussian processes Convergence Algorithm design and analysis Design optimization

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Model-based motion clustering using boosted mixture modeling

Model-based motion clustering using boosted mixture modeling

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conference on computer vision and pattern recognition (cvpr)

作者： V. Pavlovic Department of Computer Science Rutgers University Piscataway NJ USA

Model-based clustering of motion trajectories can be posed as the problem of learning an underlying mixture density function whose components correspond to motion classes with different statistical properties. We propose a general framework for boosted modeling of mixtures of parametric densities. A density is represented as a parametric mixture of kernels where mixture components are now being added recursively, one at a time, until a best fit to data occurs and an optimal number of mixture components is selected. Optimal ML and MAP solutions to this problem are found using the functional gradient techniques. Unlike traditional mixture modeling techniques, the new method does not rely on random parameter initialization and exhaustive exploration of varying model orders (such as the number of mixture components.) The method justifies parameter estimation of new mixture components independently of that of the rest of the mixture, thus allowing tractable use of complex kernels such as HMMs or switching linear dynamic models. The relationship to traditional parametric EM-based mixture modeling algorithms is established We demonstrate the utility of the new algorithm on the problem of discovering motion sequence clusters. Our generative modeling framework has an important advantage over nonparametric approaches in that it can be used for classification as well as synthesis of the learned motion categories.

关键词： Kernel Character generation Hidden Markov models Clustering algorithms Recursive estimation computer science Density functional theory Parameter estimation Motion analysis pattern analysis

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Multi-scale visual tracking by sequential belief propagation

Multi-scale visual tracking by sequential belief propagation

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conference on computer vision and pattern recognition (cvpr)

作者： Gang Hua Ying Wu Department of Electrical & Computer Engineering Northwestern University Evanston IL USA

A novel statistical method is proposed in this paper to overcome abrupt motion for robust visual tracking. Existing tracking methods that are based on the small motion assumption are vulnerable to abrupt motion, which may be induced by various factors, such as the unexpected dynamics changes of the target, frame dropping and camera motion, etc. Although with computational benefits, methods based on hierarchical search is inadequate to this problem because the propagation of the searching error may end up with bad estimates in fine scales. Since different scales contain different salient image features, we propose a new formulation in which searching and matching will be done collaboratively in different scales. The theoretical foundation of this new approach is based on dynamic Markov networks, where the bi-directional propagation of the belief of the target's posteriors on different scales reveals the collaboration among them. A nonparametric sequential belief propagation algorithm for the dynamic Markov network is developed by implementing the collaboration of a set of particle filters. Extensive experiments have demonstrated the effectiveness and efficiency of the proposed method to cope with various types of abrupt motions.

关键词： Belief propagation Collaboration Target tracking Markov random fields Statistical analysis Robustness Cameras Bidirectional control Heuristic algorithms Particle filters

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Hidden semantic concept discovery in region based image retrieval

Hidden semantic concept discovery in region based image retr...

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conference on computer vision and pattern recognition (cvpr)

作者： Ruofei Zhang Zhongfei Zhang Department of Computer Science State University of New York Binghamton Binghamton NY USA

This work addresses content based image retrieval (CBIR), focusing on developing a hidden semantic concept discovery methodology to address effective semantics-intensive image retrieval. In our approach, each image in the database is segmented to region; associated with homogenous color, texture, and shape features. By exploiting regional statistical information in each image and employing a vector quantization method, a uniform and sparse region-based representation is achieved. With this representation a probabilistic model based on statistical-hidden-class assumptions of the image database is obtained, to which expectation-maximization (EM) technique is applied to analyze semantic concepts hidden in the database. An elaborated retrieval algorithm is designed to support the probabilistic model. The semantic similarity is measured through integrating the posterior probabilities of the transformed query image, as well as a constructed negative example, to the discovered semantic concepts. The proposed approach has a solid statistical foundation and the experimental evaluations on a database of 10,000 general-purposed images demonstrate its promise of the effectiveness.

关键词： Image retrieval Image databases Content based retrieval Spatial databases Image segmentation Shape Vector quantization Image analysis Algorithm design and analysis Probability

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Names and faces in the news

Names and faces in the news

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conference on computer vision and pattern recognition (cvpr)

作者： T.L. Berg A.C. Berg J. Edwards M. Maire R. White Yee-Whye Teh E. Learned-Miller D.A. Forsyth Computer Science Division University of California Berkeley Berkeley CA USA

We show quite good face clustering is possible for a dataset of inaccurately and ambiguously labelled face images. Our dataset is 44,773 face images, obtained by applying a face finder to approximately half a million captioned news images. This dataset is more realistic than usual face recognition datasets, because it contains faces captured "in the wild" in a variety of configurations with respect to the camera, taking a variety of expressions, and under illumination of widely varying color. Each face image is associated with a set of names, automatically extracted from the associated caption. Many, but not all such sets contain the correct name. We cluster face images in appropriate discriminant coordinates. We use a clustering procedure to break ambiguities in labelling and identify incorrectly labelled faces. A merging procedure then identifies variants of names that refer to the same individual. The resulting representation can be used to label faces in news images or to organize news pictures by individuals present. An alternative view of our procedure is as a process that cleans up noisy supervised data. We demonstrate how to use entropy measures to evaluate such procedures.

关键词： Face recognition Noise Entropy

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An affine invariant tensor dissimilarity measure and its applications to tensor-valued image segmentation

An affine invariant tensor dissimilarity measure and its app...

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conference on computer vision and pattern recognition (cvpr)

作者： Zhizhou Wang B.C. Vemuri Department of CISE University of Florida Gainesville FL USA

Tensor fields specifically, matrix valued data sets, have recently attracted increased attention in the fields of image processing, computer vision, visualization and medical imaging. In this paper, we present a novel definition of tensor "distance" grounded in concepts from information theory and incorporate it in the segmentation of tensor-valued images. In some applications, a symmetric positive definite (SPD) tensor at each point of a tensor valued image can be interpreted as the covariance matrix of a local Gaussian distribution. Thus, a natural measure of dissimilarity between SPD tensors would be the KL divergence or its relative. We propose the square root of the J-divergence (symmetrized KL) between two Gaussian distributions corresponding to the tensors being compared that leads to a novel closed form expression. Unlike the traditional Frobenius norm-based tensor distance, our "distance" is affine invariant, a desirable property in many applications. We then incorporate this new tensor "distance" in a region based active contour model for bimodal tensor field segmentation and show its application to the segmentation of diffusion tensor magnetic resonance images (DT-MRI) as well as for the texture segmentation problem in computer vision. Synthetic and real data experiments are shown to depict the performance of the proposed model.

关键词： Tensile stress Image segmentation Application software computer vision Gaussian distribution Magnetic field measurement Image processing Data visualization Biomedical imaging Information theory

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Linear projection methods in face recognition under unconstrained illuminations: a comparative study

Linear projection methods in face recognition under unconstr...

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conference on computer vision and pattern recognition (cvpr)

作者： Qi Li Jieping Ye C. Kambhamettu Video/Image Modeling and Synthesis Laboratory Computer Information & Science University of Delaware Newark DE USA Computer Science & Engineering University of Minnesota Minneapolis MN USA

Face recognition under unconstrained illuminations (FR/I) received extensive study because of the existence of illumination subspace. P. Belhumer et al. (1996) presented a study on the comparison between principal component analysis (PCA) and subspace linear discriminant analysis (LDA) for this problem. PCA and subspace LDA are two well-known linear projection methods that can be characterized as trace optimization on scatter matrices. Generally, a linear projection method can be derived by applying a specific matrix analysis technique on specific scatter matrices under some optimization criterion. Several novel linear projection methods were proposed recently using generalized singular value decomposition or QR decomposition matrix analysis techniques [H. Park, et al., 2003], [J. Ye and Q. Li, 2004]. In this paper, we present a comparative study on these linear projection methods in FR/I. We further involve multiresolution analysis in the study. Our comparative study is expected to give a relatively comprehensive view on the performance of linear projection methods in FR/I problems.

关键词： Face recognition Lighting Linear discriminant analysis Principal component analysis Scattering Matrix decomposition Optimization methods Matrix converters Singular value decomposition computer science

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Super-resolution through neighbor embedding

Super-resolution through neighbor embedding

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conference on computer vision and pattern recognition (cvpr)

作者： Hong Chang Dit-Yan Yeung Yimin Xiong Department of Computer Science Hong Kong University of Science and Technology Kowloon Hong Kong China

In this paper, we propose a novel method for solving single-image super-resolution problems. Given a low-resolution image as input, we recover its high-resolution counterpart using a set of training examples. While this formulation resembles other learning-based methods for super-resolution, our method has been inspired by recent manifold teaming methods, particularly locally linear embedding (LLE). Specifically, small image patches in the lowand high-resolution images form manifolds with similar local geometry in two distinct feature spaces. As in LLE, local geometry is characterized by how a feature vector corresponding to a patch can be reconstructed by its neighbors in the feature space. Besides using the training image pairs to estimate the high-resolution embedding, we also enforce local compatibility and smoothness constraints between patches in the target high-resolution image through overlapping. Experiments show that our method is very flexible and gives good empirical results.

关键词： Image resolution Spatial resolution Interpolation Application software Smoothing methods Learning systems Geometry Image generation Layout Web pages

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"Corefaces" - robust shift invariant PCA based correlation filter for illumination tolerant face recognition

"Corefaces" - robust shift invariant PCA based correlation f...

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conference on computer vision and pattern recognition (cvpr)

作者： M. Sawides B.V.K.V. Kumar P.K. Khosla Dept. of Electr. & Comput. Eng. Carnegie Mellon Univ. Pittsburgh PA USA Electrical and Computer Engineering Dept Carnegie Mellon University Pittsburgh PA USA

In this paper we present a novel method for performing robust illumination-tolerant face recognition. We show that this method works well even when presented with partial test faces which are also captured under variable illumination and outperforms other competing face recognition algorithms. Our method is a hybrid PCA-correlation filter which links the best of two major approaches in face recognition; principal component analysis (PCA) for capturing the variability in a set of training images and advanced correlation filters which have attractive features such as illumination tolerance, shift-invariance, and can handle occlusions. We examine how these filters work and why our proposed method is able to perform better. We call our method 'Corefaces' as it seeks to model the 'core' face representation that remains relatively invariant to illumination variations. We show comparative results using the illumination subset of CMU-PIE database consisting of 65 people, and Yale-B illumination database and compare with other standard methods such as the illumination subspace method and Fisherfaces.

关键词： Principal component analysis Lighting Face recognition Face detection Matched filters Testing Image databases Humans Detectors Image storage

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Facial event classification with task oriented dynamic Bayesian network

Facial event classification with task oriented dynamic Bayes...

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conference on computer vision and pattern recognition (cvpr)

作者： Haisong Gu Qiang Ji Department of Computer Science University of Nevada슠Reno USA Department of ECSE Rensselaer Polytechnic Institute USA

Facial events include all activities of face and facial features in spatial or temporal space, such as facial expressions, face gesture, gaze and furrow happening, etc. Developing an automated system for facial event classification is always a challenging task due to the richness, ambiguity and dynamic nature of facial expressions. This paper presents an efficient approach to real-world facial event classification. By integrating dynamic Bayesian network (DBN) with a general-purpose facial behavior description language, a task-oriented stochastic and temporal framework is constructed to systematically represent and classify facial events of interest. Based on the task oriented DBN, we can spatially and temporally incorporate results from previous times and prior knowledge of the application domain. With the top-down inference, the system can make active selection among multiple visual channels to identify the most effective sensory channels to use. With the bottom-up inference from observed evidences, the current facial event can be classified with a desired confident level via the belief propagation. We applied the task-oriented DBN framework to monitoring driver vigilance. Experimental results demonstrate the feasibility and efficiency of our approach.

关键词： Bayesian methods Facial features Face detection Robustness Gold Face recognition Application software Infrared detectors computer science Stochastic systems

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