Multi-view Face Recognition with Min-Max Modular SVMs

Through task decomposition and module combination, min-max modular support vector machines (M3-SVMs) can be successfully used for difficult pattern classification task. M3-SVMs divide the training data set of the original problem to several sub-sets, and combine them to a series of sub-problems whic...

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Bibliographic Details
Published inAdvances in Natural Computation pp. 396 - 399
Main Authors Fan, Zhi-Gang, Lu, Bao-Liang
Format Book Chapter Conference Proceeding
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2005
Springer
SeriesLecture Notes in Computer Science
Subjects
Applied sciences
Artificial intelligence
Computer science; control theory; systems
Exact sciences and technology
Face Recognition
Face Recognition Technique
Module Combination
Multiclass Support Vector Machine
Pattern recognition. Digital image processing. Computational geometry
Task Decomposition
Statistical analysis
Face recognition
Image processing
Pattern classification
Facies
Minimax method
Vector support machine
Pattern recognition
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Summary:Through task decomposition and module combination, min-max modular support vector machines (M3-SVMs) can be successfully used for difficult pattern classification task. M3-SVMs divide the training data set of the original problem to several sub-sets, and combine them to a series of sub-problems which can be trained more effectively. In this paper, we explore the use of M3-SVMs in multi-view face recognition. Using M3-SVMs, we can decompose the whole complicated problem of multi-view face recognition into several simple sub-problems. The experimental results show that M3-SVMs can be successfully used for multi-view face recognition and make the classification more accurate.
ISBN:9783540283256
3540283250
3540283234
9783540283232
ISSN:0302-9743
1611-3349
DOI:10.1007/11539117_58