The nature of statistical learning theory
The nature of statistical learning theory
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Recognition of Multi-View Faces with Feature Selection
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Journal of Cognitive Neuroscience
IEEE Transactions on Neural Networks
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Gender recognition using a min-max modular support vector machine
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
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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.