The nature of statistical learning theory
The nature of statistical learning theory
Automatic Classification of Single Facial Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Analysis of Facial Expressions: The State of the Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Face Recognition Using Active Appearance Models
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Comprehensive Database for Facial Expression Analysis
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Active Appearance Models Revisited
International Journal of Computer Vision
A 3D Facial Expression Database For Facial Behavior Research
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Robust AAM Fitting by Fusion of Images and Disparity Data
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
3D Facial Expression Recognition Based on Primitive Surface Feature Distribution
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Fast Active Appearance Model Search Using Canonical Correlation Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Support vector machines for histogram-based image classification
IEEE Transactions on Neural Networks
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
Automatic analysis of human face expression is an interesting and non-trivial problem. In the last decade, many approaches have been described for emotion recognition based on analysis of facial expression. However, little has been done in the sub-area of the recognition of facial emotion intensity levels. This paper proposes the analysis of the use of Active Appearance Models (AAMs) and Support Vector Machine (SVM) classifiers in the recognition of human facial emotion and emotion intensity levels. AAMs are known as a tool for statistical modeling of object shape/appearance or for precise object feature detection. In our case, we examine their properties as a technique for feature extraction. We analyze the influence of various facial feature data types (shape/texture/combined AAM parameter vectors) and the size of facial images on the final classification accuracy. Then, approaches to proper C-SVM classifiers (RBF kernel) training parameter adjustment are described. Moreover, an alternative way of classification accuracy evaluation using the human visual system as a reference point is discussed. Unlike the usual to the approach evaluation of recognition algorithms (based on comparison of final classification accuracies), the proposed evaluation schema is independent of the testing set parameters, such as number, age and gender of subjects or the intensity of their emotions. Finally, we show that our automatic system gives emotion categories for images more consistent labels than human subjects, while humans are more consistent in identifying emotion intensity level compared to our system.