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Designing affine transformations based face recognition algorithms

Designing affine transformations based face recognition algo...

引用

2005 IEEE computer Society conference on computer vision and pattern recognition, CVPR 2005 - workshops

作者： Mohanty, Pranab K. Sarkar, Sudeep Kasturi, Rangachar Computer Science and Engineering University of South Florida TampaFL33620 United States

ISBN: (纸本)0769526608

We investigate methods to infer the best affine transformation based face recognition algorithm;which operates by projecting given images to a low-dimensional space, followed by distance computations. This category includes the following well known methods for recognition: the Principal Component Analysis (PCA), Linear Discriminant Analysis(LDA), and Independent Component Analysis (ICA). The desired affine transformation is not restricted to that which results in an orthogonal space and can involve shear and stretch. We adopt an approach that has a reverse engineering flavor. Starting from distances computed by any face recognition algorithm, such as the FRGC baseline algorithm, we learn the best affine transform that approximates it. We propose a closed form solution for this based on classical Multidimensional Scaling (MDS). Next, this affine transform is refined by considering the modification of a given distance matrix, which will enhance the separation of match and non-match scores. The affine transform that produces the best Receiver Operating Characteristic (ROC) is selected. The data from Face recognition Grand Challenge (FRGC-v2.0) reveals that learned affine transformation results in a better performance than the FRGC baseline algorithm. © 2005 IEEE computer Society. All rights reserved.

关键词： Reverse engineering

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Ear biometrics using 2D and 3D images

Ear biometrics using 2D and 3D images

引用

2005 IEEE computer Society conference on computer vision and pattern recognition, CVPR 2005 - workshops

作者： Yan, Ping Bowyer, Kevin W. Department of Computer Science and Engineering University of Notre DameIN46556 United States

ISBN: (纸本)0769526608

We present results of the largest experimental investigation of ear biometrics to date. Approaches considered include a PCA ("eigen-ear") approach with 2D intensity images, achieving 63.8% rank-one recognition;a PCA approach with range images, achieving 55.3%;Hausdorff matching of edge images from range images, achieving 67.5%, and ICP matching of the 3D data, achieving 84.1%. ICP based matching not only achieves the best performance, but also shows good scalability with size of dataset. The data set used represents over 300 persons, each with images acquired on at least two different dates. © 2005 IEEE computer Society. All rights reserved.

关键词： Principal component analysis

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A theory for photometric self-calibration of multiple overlapping projectors and cameras

A theory for photometric self-calibration of multiple overla...

引用

2005 IEEE computer Society conference on computer vision and pattern recognition, CVPR 2005 - workshops

作者： Song, Peng Cham, Tat-Jen School of Computer Engineering Nanyang Technological University Singapore639798 Singapore Singapore-MIT Alliance Singapore

ISBN: (纸本)0769526608

A theory for photometric calibration of cameras and multiple projectors with overlapping displays is presented. The theory is predominantly based on the analysis of isointensity curves in projector input state space - curves which define different projector input intensity combinations that result in the same camera-observed pixel intensities. Three methods, which have different speed and accuracy tradeoffs, are proposed for recovering the projector-to-screen and screen-to-camera intensity transfer functions. The methods do not require a specific parametric model for the shapes of these functions, nor impose any smoothness constraints. Additional methods are described for calibrating projector offsets and binary light sources, and also extending the per-pixel analysis to other pixels in the display. The methods do not require the use of expensive equipment, and may be carried out with a low dynamic range camera with minimal controls. © 2005 IEEE computer Society. All rights reserved.

关键词： Cameras

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Repeated measures GLMM estimation of subject-related and false positive threshold effects on human face verification performance

Repeated measures GLMM estimation of subject-related and fal...

引用

2005 IEEE computer Society conference on computer vision and pattern recognition, CVPR 2005 - workshops

作者： Givens, Geof H. Beveridge, J. Ross Draper, Bruce A. Phillips, P. Jonathon Statistics Department Colorado State University Computer Science Department Colorado State University NIST

ISBN: (纸本)0769526608

Subject covariate data were collected on 1, 072 pairs of FERET images for analysis in a human face verification experiment. The subject data included information about facial hair, bangs, eyes, gender, and age. The verification experiment was replicated at seven different false alarm rates ranging from 1/10, 000 to 1/100. A generalized linear mixed model (GLMM) was fit to the binary outcomes indicating correct verification. Statistically significant main effects for bangs, eyes, gender, and age were found. The effect of the log false positive rate on verification success was found to interact significantly with bangs, gender, and age. These results have important implications for future evaluation of biometrics, and the GLMM methodology used here is shown to be effective and informative for this sort of data. © 2005 IEEE computer Society. All rights reserved.

关键词： Face recognition

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Video-based human action classification with ambiguous correspondences

Video-based human action classification with ambiguous corre...

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2005 IEEE computer Society conference on computer vision and pattern recognition, CVPR 2005 - workshops

作者： Feng, Zhou Cham, Tat-Jen School of Computer Engineering Nanyang Technological University Singapore639798 Singapore Singapore-MIT Alliance Singapore

ISBN: (纸本)0769526608

This paper describes a combined tracking-classification framework for the unsupervised classification of human action. While most existing approaches assume that feature-wise correspondences on people are either available or not at all, this method explicitly formalizes how the probability of correspondences can be used in computation when the correspondences are ambiguous. It is also able to exploit in a probabilistic manner any foreground-background preprocessed segmentation, even if the segmentation is of low confidence. A principled analysis of the problem leads to a novel probabilistic action representation called the correspondence-ambiguous feature histogram array (CAFHA) that is robust to variations across similar actions. Our results show that the new framework outperforms the recent Zelnik-Manor & Irani method [19] for unsupervised event classification. Additionally, the framework is extended to quasi real-time action inference, achieving good recognition accuracy despite changes in person identity and variations in the actions. © 2005 IEEE computer Society. All rights reserved.

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Dynamic panoramic surround map: Motivation and omni video based approach

Dynamic panoramic surround map: Motivation and omni video ba...

引用

2005 IEEE computer Society conference on computer vision and pattern recognition, CVPR 2005 - workshops

作者： Gandhi, Tarak Trivedi, Mohan M. Computer Vision and Robotics Research Laboratory University of California San Diego San DiegoCA92093 United States

ISBN: (纸本)0769526608

Awareness of what surrounds a vehicle directly affects the safe driving and maneuvering of an automobile. Surround information or maps can help in ethnographic studies of driver behavior as well as provide a critical input in the development of effective driver assistance system. In this paper, we introduce the concept of Dynamic Panoramic Surround (DPS) map that shows the nearby surroundings of the vehicle, and detects the objects of importance on the road. Omnidirectional cameras which give a panoramic view of the surroundings can be useful for visualizing and analyzing the nearby surroundings of the vehicle. A novel approach for synthesizing the DPS using stereo and motion analysis of video images from a pair of omni-directional cameras on the vehicle is developed. Successful generation of DPS in experimental runs on an instrumented vehicle testbed is demonstrated. These experiments prove the basic feasibility and show promise of omni video based DPS capture algorithm to provide useful semantic descriptors of the state of moving vehicles and obstacles in the vicinity of a vehicle. © 2005 IEEE computer Society. All rights reserved.

关键词： Object detection

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An illuminance-reflectance model for nonlinear enhancement of color images

An illuminance-reflectance model for nonlinear enhancement o...

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2005 IEEE computer Society conference on computer vision and pattern recognition, CVPR 2005 - workshops

作者： Tao, Li Tompkins, Richard Asari, Vijayan K. Department of Electrical and Computer Engineering Old Dominion University NorfolkVA23529 United States

ISBN: (纸本)0769526608

An image enhancement algorithm based on illuminance-reflectance model is proposed for improving the visual quality of digital images captured under insufficient and/or non-uniform lighting conditions. The paper presents computational methods for estimation of scene illuminance and reflectance, adaptive dynamic range compression of illuminance, and adaptive enhancement for mid-tone frequency components. The images are processed in a similar way as human eyes sensing a scene. The algorithm demonstrates high quality of enhanced images, robust performance and fast processing speed. Compared with Retinex and multi-scale retinex with color restoration (MSRCR), the proposed method shows a better balance between luminance enhancement and contrast enhancement as well as a more consistent and reliable color rendition without introducing incorrect colors. This is an effective technique for image enhancement with simple computational procedures, which makes real-time application successfully realized. The application of this image enhancement technique to the FRGC images yields improved face recognition results. © 2005 IEEE computer Society. All rights reserved.

关键词： Image enhancement

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Fast selection of linear features in image data

Fast selection of linear features in image data

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2005 IEEE computer Society conference on computer vision and pattern recognition, CVPR 2005 - workshops

作者： Wu, Feng Schweitzer, Haim The University of Texas at Dallas Department of Computer Science RichardsonTX75083 United States

ISBN: (纸本)0769526608

Identifying a small number of useful features for learning from images requires processing large amounts of data and may be very time consuming. The standard approach is to compute many features from training data and then select a subset of the features that is useful for the task at hand. We propose an algorithm for selecting these features efficiently for the case in which the features are linear. Our approach is based on Principal Components dimensionality reduction applied to the training data. It is shown that the computation of linear features as well as the task of pruning redundant features can all be performed in a compact, reduced representation. Specifically, redundant features are detected and removed using the classic TU Factorization algorithm, applied in the reduced space. © 2005 IEEE computer Society. All rights reserved.

关键词： Feature extraction

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Matching tensors for pose invariant automatic 3D face recognition

Matching tensors for pose invariant automatic 3D face recogn...

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2005 IEEE computer Society conference on computer vision and pattern recognition, CVPR 2005 - workshops

作者： Mian, A.S. Bennamoun, M. Owens, R.A. School of Computer Science and Software Engineering The University of Western Australia CrawleyWA6009 Australia

ISBN: (纸本)0769526608

The face is an easily collectible and non-intrusive biometric used for the authentication and identification of individuals. 2D face recognition techniques are sensitive to changes in illumination, makeup and pose. We present a fully automatic 3D face recognition algorithm that overcomes these limitations. During the enrollment, 3D faces in the gallery are represented by third order tensors which are indexed by a 4D hash table. During online recognition, tensors are computed for a probe and are used to cast votes to the tensors in the gallery using the hash table. Gallery faces are ranked according to their votes and a similarity measure based on a linear correlation coefficient and registration error is calculated only for the high ranked faces. The face with the highest similarity is declared as the recognized face. Experiments were performed on a database of 277 subjects and a rank one recognition rate of 86.4% was achieved. Our results also show that our algorithm's execution time is insensitive to the gallery size. © 2005 IEEE computer Society. All rights reserved.

关键词： Tensors

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Sign classification using local and meta-features

Sign classification using local and meta-features

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2005 IEEE computer Society conference on computer vision and pattern recognition, CVPR 2005 - workshops

作者： Mattar, Marwan A. Hanson, Allen R. Learned-Miller, Erik G. Computer Vision Laboratory Department of Computer Science University of Massachusetts AmherstMA01003 United States

ISBN: (纸本)0769526608

Our world is populated with visual information that a sighted person makes use of daily. Unfortunately, the visually impaired are deprived from such information, which limits their mobility in unconstrained environments. To help alleviate this we are developing a wearable system [1, 19] that is capable of detecting and recognizing signs in natural scenes. The system is composed of two main components, sign detection and recognition. The sign detector, uses a conditional maximum entropy model to find regions in an image that correspond to a sign. The sign recognizer matches the hypothesized sign regions with sign images in a database. The system decides if the most likely sign is correct or if the hypothesized sign region does not belong to a sign in the database. Our data sets encompass a wide range of variability including changes in lighting, orientation and viewing angle. In this paper, we present an overview of the system while while paying particular attention to the recognition component. Tested on 3,975 sign images from two different data sets, the recognition phase achieves accuracies of 99.5% with 35 distinct signs and 92.8% with 65 distinct signs. © 2005 IEEE computer Society. All rights reserved.

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