Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Regularized multi--task learning
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Journal of Cognitive Neuroscience
A model of inductive bias learning
Journal of Artificial Intelligence Research
Radial Basis Function Network for Multitask Pattern Recognition
Neural Processing Letters
View construction for multi-view semi-supervised learning
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part I
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part III
Improved machine learning models for predicting selective compounds
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
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For traditional human face based biometrics, usually one task (face recognition) is learned at one time. This single task learning (STL) approach may neglect potential rich information resources hidden in other related tasks, while multitask learning (MTL) can make full use of the latent information. MTL is an inductive transfer method which improves generalization by using the domain information contained in the training signals of related tasks as an inductive bias. In this paper, backpropagation (BP) network based MTL approach is proposed for face recognition. The feasibility of this approach is demonstrated through two different face recognition experiments, which show that MTL based on BP neural networks is more effective than the traditional STL approach, and that MTL is also a practical approach for face recognition.