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Enhanced Biologically Inspired Model

Enhanced Biologically Inspired Model

引用

26th IEEE Conference on Computer Vision and pattern recognition (CVPR 2008), vol.7

作者： Yongzhen Huang Kaiqi Huang Liangsheng Wang Dacheng Tao Tieniu Tan Xuelong Li National Laboratory of Pattern Recognition Institute of Automation Chinese Academy and Sciences Beijing China Biometrics Research Centre Department of Computing Hong Kong Polytechnic University Hong Kong China School of Computer Science and Information Systems Birkbeck University of London London UK

It has been demonstrated by Serre et al. that the biologically inspired model (BIM) is effective for object recognition. It outperforms many state-of-the-art methods in challenging databases. However, BIM has the following three problems: a very heavy computational cost due to dense input, a disputable pooling operation in modeling relations of the visual cortex, and blind feature selection in a feedforward framework. To solve these problems, we develop an enhanced BIM (EBIM), which removes uninformative input by imposing sparsity constraints, utilizes a novel local weighted pooling operation with stronger physiological motivations, and applies a feedback procedure that selects effective features for combination. Empirical studies on the CalTech5 database and CalTech101 database show that EBIM is more effective and efficient than BIM. We also apply EBIM to the MIT-CBCL street scene database to show it achieves comparable performance in comparison with the current best performance. Moreover, the new system can process images with resolution 128 × 128 at a rate of 50 frames per second and enhances the speed 20 times at least in comparison with BIM in common applications.

关键词： Biological system modeling Spatial databases Image databases Brain modeling Object recognition Visual databases Computational efficiency Feedforward systems Feedback Layout

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Ordinal representations for biometrics recognition

Ordinal representations for biometrics recognition

引用

15th European Signal Processing Conference, EUSIPCO 2007

作者： Tan, Tieniu Sun, Zhenan Center for Biometrics and Security Research National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences P.O. Box 2728 Beijing 100080 China

ISBN: (纸本)9788392134022

biometrics provides a reliable method for automatic personal identification and has wide and important applications. The success of a biometric recognition system depends critically on its feature representation model for biometric patterns. The most challenging issue in biometric feature representation is to achieve sensitivity to inter-class differences and at the same time robustness against intra-class variations. Many biometric representation schemes have been reported but the above issue remains to be resolved. In this paper, we introduce ordinal measures for iris, face and palmprint image representation in an attempt to resolve this issue. With this so-called ordinal representation model in place, many best-performing biometric recognition methods may be interpreted as special cases of this model. In this sense, the proposed ordinal representation model forms a general framework for biometric pattern representation. Extensive experimental results on public biometric databases demonstrate the effectiveness and generality of this representation. © 2007 EURASIP.

关键词： biometrics

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Fusion of Face and Palmprint for Personal Identification Based on Ordinal Features

Fusion of Face and Palmprint for Personal Identification Bas...

引用

Conference on Computer Vision and pattern recognition (CVPR)

作者： Rufeng Chu Shengcai Liao Yufei Han Zhenan Sun Stan Z. Li Tieniu Tan Center for Biometrics and Security Research & National Laboratory of Pattern Recognition Institute of Automation Chinese Academy and Sciences China

In this paper, we present a face and palmprint multimodal biometric identification method and system to improve the identification performance. Effective classifiers based on ordinal features are constructed for faces and palmprints, respectively. Then, the matching scores from the two classifiers are combined using several fusion strategies. Experimental results on a middle-scale data set have demonstrated the effectiveness of the proposed system.

关键词： biometrics Face detection Lighting Sun Information security National security Laboratories pattern recognition Automation Authentication

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Real-time Object Classification in Video Surveillance Based on Appearance Learning

Real-time Object Classification in Video Surveillance Based ...

引用

Conference on Computer Vision and pattern recognition (CVPR)

作者： Lun Zhang Stan Z. Li Xiaotong Yuan Shiming Xiang Center for Biometrics and Security Research & National Laboratory of Pattern Recognition Institute of Automation Chinese Academy and Sciences Beijing China

Classifying moving objects to semantically meaningful categories is important for automatic visual surveillance. However, this is a challenging problem due to the factors related to the limited object size, large intra-class variations of objects in a same class owing to different viewing angles and lighting, and real-time performance requirement in real-world applications. This paper describes an appearance-based method to achieve real-time and robust objects classification in diverse camera viewing angles. A new descriptor, i.e., the multi-block local binary pattern (MB-LBP), is proposed to capture the large-scale structures in object appearances. Based on MB-LBP features, an adaBoost algorithm is introduced to select a subset of discriminative features as well as construct the strong two-class classifier. To deal with the non-metric feature value of MB-LBP features, a multi-branch regression tree is developed as the weak classifiers of the boosting. Finally, the error correcting output code (ECOC) is introduced to achieve robust multi-class classification performance. Experimental results show that our approach can achieve real-time and robust object classification in diverse scenes.

关键词： Video surveillance Cameras Robustness Vehicles Classification tree analysis Layout Object recognition Shape Large-scale systems Error correction codes

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Part-based Face recognition Using Near Infrared Images

Part-based Face Recognition Using Near Infrared Images

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Conference on Computer Vision and pattern recognition (CVPR)

作者： Ke Pan Shengcai Liao Zhijian Zhang Stan Z. Li Peiren Zhang University of Science and Technology Hefei China Center for Biometrics and Security Research & National Laboratory of Pattern Recognition Institute of Automation Chinese Academy and Sciences Beijing China

Recently, the authors developed NIR based face recognition for highly accurate face recognition under illumination variations. In this paper, we present a part-based method for improving its robustness with respect to pose variations. An NIR face is decomposed into parts. A part classifier is built for each part, using the most discriminative LBP histogram features selected by AdaBoost learning. The outputs of part classifiers are fused to give the final score. Experiments show that the present method outperforms the whole face-based method by 4.53%.

关键词： Face recognition Infrared imaging Face detection Lighting Robustness Histograms Support vector machines Support vector machine classification Image recognition Biomedical optical imaging

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A Random Field Model for Improved Feature Extraction and Tracking

A Random Field Model for Improved Feature Extraction and Tra...

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IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS)

作者： Xiaotong Yuan Stan Z. Li Center for Biometrics and Security Research & National Laboratory of Pattern Recognition Chinese Academy of Science Institute of Automation Chinese Academy and Sciences Beijing China Center for Biometrics and Security Research & National Laboratory of Pattern Recognition Institute of Automation Chinese Academy and Sciences Beijing China

This paper presents a novel method for illumination-invariant and contrast preserving feature extraction, aimed at improving performance of tracking under complex light condition. Features to be extracted are represented as a weight field. An energy function of the field is defined as an approximate variance in robust statistics. A simple non-linear iterative rule is derived to compute the optimal field. The optimal field is shown to be invariant to global illumination switching, and preserving target/background contrast. We incorporate the feature extraction method into a mean-shift tracker and this achieves reliable results on real-world sequences in complex scenes and varying illumination.

关键词： Feature extraction Target tracking Lighting Robustness Photometry Switches Image edge detection biometrics National security Laboratories

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Iris Localization via Pulling and Pushing

Iris Localization via Pulling and Pushing

引用

International Conference on pattern recognition

作者： Zhaofeng He Tieniu Tan Zhenan Sun Center for Biometrics and Security Research & National Laboratory of Pattern Recognition Institute of Automation Chinese Academy and Sciences China

Iris localization is a critical module in iris recognition because it defines the inner and outer boundaries of iris region used for feature analysis. State-of-the-art iris localization methods need to implement a brute-force search of the large parameter space, which is time-consuming and sensitive to noises. This paper proposes a novel iris localization method based on a spring force-driven iteration scheme. First, the coarse localization of pupil is obtained by an AdaBoost-based iris detection method. Then the radial edge points are detected in polar coordinate, which contribute a force on the circle center based on Hooke's law. Finally the center and radius of pupil and iris are refined according to the composition of forces from all points. After 3-4 iterations, the precise location of iris could be found. Experimental results show that our method is faster and more accurate than state-of-the-art iris localization methods

关键词： Springs biometrics Iris recognition Force measurement pattern recognition Security Feature extraction Iterative algorithms Image restoration Helium

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Learning Feature Extraction and Classification for Tracking Multiple Objects: A Unified Framework

Learning Feature Extraction and Classification for Tracking ...

引用

IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS)

作者： Xiaotong Yuan Stan Z. Li Center for Biometrics and Security Research & National Laboratory of Pattern Recognition Institute of Automation Chinese Academy and Sciences Beijing China

A great challenge in tracking multiple objects is how to locate each object when they interact and form a group. We view it as a binary classification problem. It is important to base the classification on the currently most discriminative features. We derive a unified framework for learning feature extraction and classification in appearance-spatial space for multiple object tracking. In this framework, both classifier design and feature evaluation are accomplished by minimizing an criterion which corresponds to an upperbound of classification error. There, the most discriminative features, as variables, minimize the criterion function, whereas the classifier, as a function, minimizes the criterion functional. The resulting system offers high accuracy for real-time tracking of nearby multiple objects in complex and dynamic scenes.

关键词： Feature extraction Layout Bayesian methods Surveillance Object detection Error analysis biometrics National security Laboratories pattern recognition

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A near-infrared image based face recognition system

A near-infrared image based face recognition system

引用

International Conference on Automatic Face and Gesture recognition

作者： S.Z. Li Lun Zhang ShengCai Liao XiangXin Zhu RuFeng Chu Meng Ao Ran He Center for Biometrics and Security Research & National Laboratory of Pattern Recognition Institute of Automation Chinese Academy and Sciences Beijing China

In this paper, we present a near infrared (NIR) image based face recognition system. Firstly, we describe a design of NIR image capture device which minimizes influence of environmental lighting on face images. Both face and facial feature localization and face recognition are performed using local features with AdaBoost learning. An evaluation in real-world user scenario shows that the system achieves excellent accuracy, speed and usability

关键词： Face recognition Face detection Image recognition Facial features Infrared imaging Lighting Infrared detectors Infrared sensors Temperature sensors Performance analysis

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Key Techniques and Methods for Imaging Iris in Focus

Key Techniques and Methods for Imaging Iris in Focus

引用

International Conference on pattern recognition

作者： Yuqing He Jiali Cui Tieniu Tan Yangsheng Wang Center for Biometrics and Security Research National Laboratory of Pattern Recognition Institute of Automation Chinese Academy and Sciences Beijing China School of Information Science and Technology Beijing Institute of Technology Beijing China

Automated iris recognition is a promising method for noninvasive verification of identity. How to acquire an iris image in focus is a key issue in iris recognition. Based on imaging properties of a simple lens, working principles of fixed focus and auto focus imaging systems are described. Key techniques for imaging iris in focus are discussed in this paper, such as illumination, lens design and self alignment to position the iris in the system's depth of field. According to the techniques, a clear iris acquisition system is developed and experimental results are presented in this paper

关键词： Focusing Optical imaging Lenses Iris recognition Image recognition Optical sensors biometrics pattern recognition Optical control Optical design

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