Bayesian Classification With Gaussian Processes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Classification of Single Facial Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing Action Units for Facial Expression Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian approach for neural networks—review and case studies
Neural Networks
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Bayesian parameter estimation via variational methods
Statistics and Computing
Advanced lectures on machine learning
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
A facial expression recognition system based on supervised locally linear embedding
Pattern Recognition Letters
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Facial expression recognition based on Local Binary Patterns: A comprehensive study
Image and Vision Computing
Kernel Adaptive Filtering: A Comprehensive Introduction
Kernel Adaptive Filtering: A Comprehensive Introduction
Facial expression recognition using kernel canonical correlation analysis (KCCA)
IEEE Transactions on Neural Networks
Fusion of feature sets and classifiers for facial expression recognition
Expert Systems with Applications: An International Journal
Computational Intelligence and Neuroscience
Proceedings of the 2nd Workshop on Machine Learning for Interactive Systems: Bridging the Gap Between Perception, Action and Communication
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The Gaussian process (GP) approaches to classification synthesize Bayesian methods and kernel techniques, which are developed for the purpose of small sample analysis. Here we propose a GP model and investigate it for the facial expression recognition in the Japanese female facial expression dataset. By the strategy of leave-one-out cross validation, the accuracy of the GP classifiers reaches 93.43% without any feature selection/extraction. Even when tested on all expressions of any particular expressor, the GP classifier trained by the other samples outperforms some frequently used classifiers significantly. In order to survey the robustness of this novel method, the random trial of 10-fold cross validations is repeated many times to provide an overview of recognition rates. The experimental results demonstrate a promising performance of this application.