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Personal verification using palmprint and hand geometry biometric

4th International Conference on Audio and Video-Based Biometric Person Authentication, AVBPA 2003

作者： Kumar, Ajay Wong, David C. M. Shen, Helen C. Jain, Anil K. Department of Computer Science Hong Kong University of Science and Technology Clear Water Bay Hong Kong Pattern Recognition and Image Processing Lab Department of Computer Science and Engineering Michigan State University East LansingMI48824 United States

ISBN: (纸本)9783540403029

A new approach for the personal identification using hand images is presented. This paper attempts to improve the performance of palmprint-based verification system by integrating hand geometry features. Unlike other bimodal biometric systems, the users does not have to undergo the inconvenience of passing through two sensors since the palmprint and hand geometry features can be are acquired from the same image, using a digital camera, at the same time. Each of these gray level images are aligned and then used to extract palmprint and hand geometry features. These features are then examined for their individual and combined performance. The image acquisition setup used in this work was inherently simple and it does not employ any special illumination nor does it use any pegs to cause any inconvenience to the users. Our experimental results on the image dataset from 100 users confirm the utility of hand geometry features with those from palmprints and achieve promising results with a simple image acquisition setup. © Springer-Verlag Berlin Heidelberg 2003.

关键词： image acquisition

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Analytic reduction of the Kruppa equations

Analytic reduction of the Kruppa equations

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24th German Association for pattern recognition Symposium, DAGM 2002

作者： Canterakis, Nikos Albert-Ludwigs-Universität Freiburg Computer Science Department Institute for Pattern Recognition and Image Processing FreiburgD-79110 Germany

ISBN: (纸本)354044209X

Given the fundamental matrix between a pair of images taken by a nonstationary projective camera with constant internal parameters, we show how to use the two independent Kruppa equations in order to explicitly cut down the number of parameters of the Kruppa matrix KKT by exactly two. Thus, we derive a procedure which results in a closed formula for the Kruppa matrix that depends on exactly three remaining parameters. This formula allows an easy incorporation of the positivity constraint and admits of an interpretation in terms of the image of the horopter. We focus on the general case where the camera motion is unknown and not restricted to some special type. Solutions of the Kruppa equations given three fundamental matrices have been attempted in the past by iterative numerical methods that are searching in multidimensional spaces. As an application of the reduced Kruppa matrix mentioned above we also outline how this problem can be analytically reduced to the determination of the real, positive roots of a polynomial of 14-th degree in one variable. © Springer-Verlag Berlin Heidelberg 2002.

关键词： Matrix algebra

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Hierarchical primitives based contour matching

Hierarchical primitives based contour matching

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24th Symposium of the German pattern recognition Association, DAGM 2002

作者： Zhang, Xiaofeng Burkhardt, Hans Orbotech-Schuh GmbH Co. and KG Bad-Pyrmont D-31812 Germany Department of Pattern Recognition and Image Processing Institute of Computer Science Faculty of Applied Science Freiburg Germany

ISBN: (纸本)354044209X

An algorithm for contour matching is presented in this paper. It is implemented in two steps: firstly, bottom-up, corners are matched, the matched corner points guide line segment matching, and then the matched line segments guide contour matching. Line segments are grouped with the signature function defined in [10] from the extracted contours ofimage pairs. Secondly, top-down, with the computed signature functions of the matched contours, the contour points are corresponded by modifying the initial matching from line segments. The novelty ofour approach is that (1) features are incorporated, and the matching is implemented in two steps (bottom-up and top-down), thus dense correspondences along contours are acquired. Disadvantages such as the sparseness from only point correspondences, the inaccuracies from only line correspondences, and feature loss of correspondences along epipolar lines are avoided;(2) unlike the conventional way ofmatching along epipolar lines after rectification, signature functions developed in [10] are used to characterize the contours so that the matching is implemented along contours. © Springer-Verlag Berlin Heidelberg 2002.

关键词： Contour measurement

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Multiple objects segmentation based on maximum-likelihood estimation and optimum entropy-distribution(MLE-OED)

Multiple objects segmentation based on maximum-likelihood es...

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16th International Conference on pattern recognition, ICPR 2002

作者： Jun, Xie Tsui, H.T. Deshen, Xia Image Processing and Computer Vision Lab Department of Electronic Engineering Chinese University of Hong Kong Hong Kong Pattern Recognition Lab Department of Computer Science Nanjing University of Sci.andTech. China

ISBN: (纸本)0769516963

A new method based on MLE-OED is proposed for unsupervised image segmentation of multiple objects which have fuzzy edges. It adjusts the parameters of a mixture of Gaussian distributions via minimizing a new loss function proposed to implement image segmentation based on the image's local spatial information and global intensity distribution properties . The loss function consists of two terms: a local content fitting term, which optimizes the entropy distribution, and a global statistical fitting term, which maximizes the likelihood of the parameters for the given data. The proposed segmentation method was validated by simulated and real examples Its performance in the experiments is better than those of two popular methods. © 2002 IEEE.

关键词： Maximum likelihood estimation

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Head detection and localization from sparse 3D data

Head detection and localization from sparse 3D data

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24th German Association for pattern recognition Symposium, DAGM 2002

作者： Clabian, Markus Rötzer, Harald Bischof, Horst Kropatsch, Walter Advanced Computer Vision GmbH Donau City Str.1 ViennaA-1220 Austria Institute for Computer Graphics and Vision Graz University of Technology Inffeldgasse 16 GrazA-8010 Austria Pattern Recognition and Image Processing Group Institute of Computer Aided Automation Computer Science Department Vienna University of Technology Favoritenstr. 9 ViennaA-1040 Austria

ISBN: (纸本)354044209X

Head detection is an important, but difficult task, if no restrictions such as static illumination, frontal face appearance or uniform background can be assumed. We present a system that is able to perform head detection under very general conditions by employing a 3D measurement system namely a structured light distance measurement. An algorithm of head detection from sparse 3D data (19x19 data points) is developed that reconstructs a 3D surface over the image plane and detects head hypotheses of ellipsoidal shape. We demonstrate that detection and rough localization is possible in up to 90% of the images. © Springer-Verlag Berlin Heidelberg 2002.

关键词： computers

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Text scanner with text detection technology on image sequences

Text scanner with text detection technology on image sequenc...

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16th International Conference on pattern recognition, ICPR 2002

作者： Jung, Keechul Kim, Kwang In Kurata, Takeshi Kourogi, Masakastu Han, Junghyun Pattern Recognition and Image Processing Lab. Michigan State Univerisity United States Artificial Intelligence Lab. KAIST Korea Republic of National Institute of Advanced Industrial Science and Technology Japan School of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

ISBN: (纸本)0769516963

We propose a text scanner, which detects wide text strings in a sequence of scene images. For scene text detection, we use a multiple-CAMShift algorithm on a text probability image produced by a multi-layer perceptron. To provide enhanced resolution of the extracted text images, we perform the text detection process after generating a mosaic image in a fast and robust image registration method. © 2002 IEEE.

关键词： image enhancement

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Text Scanner with text detection technology on image sequences

Text Scanner with text detection technology on image sequenc...

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作者： Jung, Keechul Kim, Kwang In Kurata, Takeshi Kourogi, Masakastu Han, JungHyun Pattern Recognition and Image Processing Lab. Michigan State Univerisity United States Artificial Intelligence Lab. KAIST Korea Republic of National Institute of Advanced Industrial Science and Technology Japan School of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

关键词： Text processing

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Multiple objects segmentation based on Maximum-likelihood Estimation and Optimum Entropy-distribution(MLE-OED)

Proceedings - International Conference on Pattern Recognitio...

引用

Proceedings - International Conference on pattern recognition 2002年第1期16卷 707-710页

A new method based on MLE-OED is proposed for unsupervised image segmentation of multiple objects which have fuzzy edges. It adjusts the parameters of a mixture of Gaussian distributions via minimizing a new loss function proposed to implement image segmentation based on the image's local spatial information and global intensity distribution properties . The loss function consists of two terms: a local content fitting term, which optimizes the entropy distribution, and a global statistical fitting term, which maximizes the likelihood of the parameters for the given data. The proposed segmentation method was validated by simulated and real examples. Its performance in the experiments is better than those of two popular methods. © 2002 IEEE.

关键词： Object recognition

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Mobile robot localization under varying illumination

Mobile robot localization under varying illumination

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作者： Jogan, Matjaž Leonardis, Aleš Wildenauer, Horst Bischof, Horst Faculty of Computer and Information Science University of Ljubljana Tržaška 25 1001 Ljubljana Slovenia Pattern Recognition and Image Processing Group Institute for Computer Aided Automation Vienna University of Technology Favoritenstrasse 9/1832 A-1040 Vienna Austria Institute for Computer Graphics and Vision Graz University of Technology Inffeldgasse 16 2. OG A-8010 Graz Austria

Methods for mobile robot localization that use eigenspaces of panoramic snapshots of the environment are in general sensitive to changes in the illumination of the environment. Therefore, we propose an approach which achieves a reliable localization under severe illumination conditions. The method uses gradient filtering of the eigenspace. After testing the approach on images obtained by a mobile robot, we show that it outperforms the standard eigenspace-based recognition method. © 2002 IEEE.

关键词： Mobile robots

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Multiple objects segmentation based on maximum-likelihood estimation and optimum entropy-distribution (MLE-OED)

Multiple objects segmentation based on maximum-likelihood es...

引用

International Conference on pattern recognition

作者： Xie Jun H.T. Tsui Xia Deshen Image Processing and Computer Vision Laboratory Department of Electronic Engineering Chinese University of Hong Kong Hong Kong China Pattern Recognition Laboratory Department of Computer Science Nanjing University of Science and Technology China

ISBN: (纸本)076951695X

A new method based on MLE-OED is proposed for unsupervised image segmentation of multiple objects which have fuzzy edges. It adjusts the parameters of a mixture of Gaussian distributions via minimizing a new loss function proposed to implement image segmentation based on the image's local spatial information and global intensity distribution properties. The loss function consists of two terms: a local content fitting term, which optimizes the entropy distribution, and a global statistical fitting term, which maximizes the likelihood of the parameters for the given data. The proposed segmentation method was validated by simulated and real examples. The performance in the experiments is better than those of two popular methods.

关键词： Object segmentation Maximum likelihood estimation image segmentation Layout Uncertainty image edge detection Shape measurement image processing pattern recognition Gaussian distribution

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