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8. Advanced techniques for face-based biometrics

8. Advanced techniques for face-based biometrics

引用

international conference on pattern recognition

作者： Massimo Tistarelli Universita di Sassari

Face recognition is nowadays one of the most challenging biometric modalities for the identification of individuals. In the last two decades several experimental as well as commercial systems have been developed exploiting different physical properties of the face image. Either being based on processing 2D or 3D information all these methods perform a face classification of the individuals based on some relevant features extracted from the raw image data. The data acquisition, preprocessing and the feature extraction/selection are all topics of the greatest importance to design a good performing recognition system. At the same time, the right choice of the features to be used as the basis for the face representation, which must be based on the uniqueness of such features, as well as most advanced issues such as the incorporation of quality information and the cope for ageing effects, are all of paramount importance. The tutorial will consists of two sessions (half day of total duration) devoted to the description of both the basic and most advanced techniques related to face recognition. The lectures will provide a comprehensive outline of face- based biometrics, its relation to biological systems (the psychophysics of the human visual system), including the existing applications and commercial systems. The lectures will provide an in-depth analysis of the state-of-the-art algorithms for face-image analysis including: face detection and tracking, landmark localization, feature extraction, face representation and classification. The lectures will mainly explore the image processing aspects of the recognition process. As for classification, machine learning algorithms will be also presented, including kernel methods as related to learning and the approximation theory. The most relevant issues and problems will be raised, providing practical solutions and algorithms responding to them. Particular attention will be given to the most advanced and new techniques for face

关键词： Biometrics Face detection Face recognition Feature extraction machine learning algorithms Aging Algorithm design and analysis data mining data acquisition Biological systems

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Embedding HMM’s-based models in a Euclidean space: The topological hidden Markov models

Embedding HMM’s-based models in a Euclidean space: The topo...

引用

international conference on pattern recognition

作者： Djamel Bouchaffra Grambling State University

ISBN: (纸本)9781424421749

One of the major limitations of HMM-based models is the inability to cope with topology: when applied to a visible observation (VO) sequence, HMM-based techniques have difficulty predicting the n-dimensional shape formed by the symbols of the VO sequence. To fulfill this need, we propose a novel paradigm named "topological hidden Markov models" (THMM's) that classifies VO sequences by embedding the nodes of an HMM state transition graph in a Euclidean space. We have applied the concept of THMM's to: (i) predict the ASCII class assigned to a handwritten numeral, and (ii) map a protein primary structure to its 3D fold. The results show that the concept of second level THMM's outperforms the SHMM's and the SVM classifiers.

关键词： Hidden Markov models Shape Topology Speech recognition Predictive models machine learning data mining Signal analysis Proteins Support vector machines

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Automata-based L-Grammar extraction from multiple images for virtual plants

Automata-based L-Grammar extraction from multiple images for...

引用

international conference on Bio-Inspired Computing: Theories and Applications, BICTA

作者： Hongchun Qu Qingsheng Zhu Lingqiu Zeng Mingwei Guo Zhonghua Lu Chongqing University Chongqing Sichuan CN Coll. of Comput. Sci. Chongqing Univ. Chongqing College of Computer Science Chongqing University Chongqing China Chongqing Agriculture Science Institute Chongqing China

L-system (Lindenmayer system) and its application have been one of the most famous and powerful tools for virtual plant modelling. But it is really hard to develop L-grammar manually for a given plant depending only on imagination or experience. For bridging this gap, a novel automatic L-grammar extraction approach is presented in this work. Initially, image processing as well as pattern recognition methods are employed to recover morphological and geometrical information for growth units and metamers. And then, these data are further analyzed using Markovian methods and acted as parameters for bidimensional hierarchical automata (BHA) to describe plant branching structure. Finally, the L-grammar has been extracted by means of the transformation from BHA to L-system. Experimental results show that our approach can extract L grammar for unfoliaged tree effectively.

关键词： Automata Hidden Markov models Pediatrics Three dimensional displays data mining Computational modeling Image reconstruction

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Training Linear Discriminant Analysis in Linear Time

Training Linear Discriminant Analysis in Linear Time

引用

IEEE 24th international conference on data Engineering (ICDE 2008), vol.1

作者： Deng Cai Xiaofei He Jiawei Han Department of Computer Science University of Illinois Urbana IL USA Yahoo Inc. Burbank CA USA

ISBN: (纸本)9781424418367;1424418364

Linear Discriminant Analysis (LDA) has been a popular method for extracting features which preserve class separability. It has been widely used in many fields of information processing, such as machine learning, data mining, information retrieval, and pattern recognition. However, the computation of LDA involves dense matrices eigen-decomposition which can be computationally expensive both in time and memory. Specifically, LDA has O(mnt + t{sup}3) time complexity and requires O(mn + mt + nt) memory, where m is the number of samples, n is the number of features and t = min(m,n). When both m and n are large, it is infeasible to apply LDA. In this paper, we propose a novel algorithm for discriminant analysis, called Spectral Regression Discriminant Analysis (SrdA). By using spectral graph analysis, SrdA casts discriminant analysis into a regression framework which facilitates both efficient computation and the use of regularization techniques. Our theoretical analysis shows that SrdA can be computed with O(ms) time and O(ms) memory, where s(≤ n) is the average number of non-zero features in each sample. Extensive experimental results on four real world data sets demonstrate the effectiveness and efficiency of our algorithm.

关键词： laser Doppler anemometry Anterior interventricular branch of left coronary artery Left-sided displacement of abomasum Linear discriminant analysis discriminant analysis Information Processing information retrieval Memory Memory dataset pattern recognition Theoretical analysis

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pattern recognition Based on Weighted and Supervised ART2

Pattern Recognition Based on Weighted and Supervised ART2

引用

2008 3rd international conference on Intelligent System and Knowledge Engineering(ISKE 2008)

作者： Chu Na, Ma Lizhuang Dept. of Computer Science & Engineering, Shanghai Jiao Tong University, Shanghai, China

It is crucial for TCM (Traditional Chinese Medicine) post-hepatitis cirrhosis diagnosis to accurately identify the syndrome. Meanwhile, the selection of features which are relevant to a certain TCM post-hepatitis cirrhosis syndrome not only improves the performance of the classifiers, but also provides well measure for treatment. Therefore, in this paper, we analyze the classical ART2(Adaptive Resonance Theory 2) neural network, such as the problem of pattern drifting and the same phase data with different amplitudes. Based on this, here, a novel network named SWART2 is proposed by taking dispersion testing and centroid computation learning, and introducing the weighted and supervised mechanism, which aims at improving ART2's ability of classification greatly for post- hepatitis cirrhosis diagnosis. Experimental results in this paper showed that the new SWART2 performed better than classical ART2.

关键词： pattern recognition Based on Weighted and Supervised ART2

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Multi-source data modelling: Integrating related data to improve model performance 1

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5th international conference on machine learning and data mining in pattern recognition

作者： Trundle, Paul R. Neagu, Daniel C. Chaudhry, Qasim Univ Bradford Richmond Rd Bradford BD7 1DP W Yorkshire England Cent Sci Lab York YO41 1LZ N Yorkshire England

ISBN: (数字)9783540734994

ISBN: (纸本)9783540734987

Traditional methods in data mining cannot be applied to all types of data with equal success. Innovative methods for model creation are needed to address the lack of model performance for data from which it is difficult to extract relationships. This paper proposes a set of algorithms that allow the integration of data from multiple datasets that are related, as well as results from the implementation of these techniques using data from the field of Predictive Toxicology. The results show significant improvements when related data is used to aid in the model creation process, both overall and in specific data ranges. The proposed algorithms have potential for use within any field where multiple datasets exist, particularly in fields combining computing, chemistry and biology.

关键词： data integration data mining machine learning multi-species modelling

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Research and application of a hierarchical fault diagnosis system based on support vector machine

Research and application of a hierarchical fault diagnosis s...

引用

3rd international conference on Natural Computation (ICNC 2007)

作者： Liu, Ailun Yuan, Xiaoyan Yu, Jinshou E China Univ Sci & Technol Res Inst Automat Shanghai 200237 Peoples R China

ISBN: (纸本)0769528759

Support Vector machine (SVM) is a kind of machine learning method based on the statistical learning theory, it has been applied in the fault diagnosis field. After analyzing SVM pattern classification theory, a hierarchical structure Fault Detection and Identification (FDI) system is presented in this paper, and simulation results show that this method can effectively handle the complex process characteristic and improve FDI model performance.

关键词： support Vector machine (SVM) fault diagnosis pattern recognition

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data analysis and bioinformatics

引用

2nd international conference on pattern recognition and machine Intelligence

作者： Di Gesu, Vito Univ Palermo CITC I-90133 Palermo Italy

ISBN: (纸本)9783540770459

data analysis methods and techniques are revisited in the case of biological data sets. Particular emphasis is given to clustering and mining issues. Clustering is still a subject of active research in several fields such as statistics, pattern recognition, and machine learning. data mining adds to clustering the complications of very large data-sets with many attributes of different types. And this is a typical situation in biology. Some cases studies are also described.

关键词： clustering data mining bio-informatics kernel methods hidden Markov models multi-layers model

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data selection using SASH trees for support vector machines 1

引用

5th international conference on machine learning and data mining in pattern recognition

作者： Sun, Chaofan Vilalta, Ricardo Univ Houston Dept Comp Sci 4800 Calhoun Rd Houston TX 77204 USA Ctr Res & Adv Studies CINVESTAV Guadalajara 45010 Jalisco Mexico

ISBN: (数字)9783540734994

ISBN: (纸本)9783540734987

This paper presents a data preprocessing procedure to select support vector (SV) candidates. We select decision boundary region vectors (BRVs) as SV candidates. Without the need to use the decision boundary, BRVs can be selected based on a vector's nearest neighbor of opposite class (NNO). To speed up the process, two spatial approximation sample hierarchical (SASH) trees are used for estimating the BRVs. Empirical results show that our data selection procedure can reduce a full dataset to the number of SVs or only slightly higher. Training with the selected subset gives performance comparable to that of the full dataset. For large datasets, overall time spent in selecting and training on the smaller dataset is significantly lower than the time used in training on the full dataset.

关键词： sampling methods support vector machines

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Study of models clustering and its application to ensemble learning

Study of models clustering and its application to ensemble l...

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3rd international conference on Natural Computation (ICNC 2007)

作者： Li, Kai Cui, Lijuan HeBei Univ Sch Math & Comp Baoding 071002 Peoples R China Hebei Univ Library Baoding 071002 Peoples R China

ISBN: (纸本)0769528759

Clustering technique is an important tool for data analysis and has a promising prospect in data mining, pattern recognition, etc. Usually, objects in clustering analysis are of vectors, which consist of some features.. They may be represented as points in Euclidean space. However, in some tasks, objects in clustering analysis may be some abstract models other than data points, for example neural networks, decision trees, support vector machines, etc. By defining the extended distance (in real tasks, there are some different definition forms about distance), clustering method is studied for the abstract data objects. Framework of clustering algorithm for objects of models is presented As its application, a method for improving diversity of ensemble learning with neural networks is investigated. The relations between the number of clusters in clustering analysis, the size of ensemble learning, and performance of ensemble learning are studied by experiments.

关键词： Clustering algorithms

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