An introduction to digital image processing
An introduction to digital image processing
Automatic Interpretation and Coding of Face Images Using Flexible Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing Action Units for Facial Expression Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Robust Real-Time Face Detection
International Journal of Computer Vision
Recognizing Facial Expression: Machine Learning and Application to Spontaneous Behavior
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Extracting eyebrow contour and chin contour for face recognition
Pattern Recognition
Facial expression recognition based on Local Binary Patterns: A comprehensive study
Image and Vision Computing
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Pattern Recognition and Information Processing Using Neural Networks;Guest Editors: Fuchun Sun,Ying Tan,Cong Wang
Real-time 2D+3D facial action and expression recognition
Pattern Recognition
A new ranking method for principal components analysis and its application to face image analysis
Image and Vision Computing
Local binary patterns for multi-view facial expression recognition
Computer Vision and Image Understanding
Human expression recognition from motion using a radial basis function network architecture
IEEE Transactions on Neural Networks
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This paper presents a novel emotion recognition model using the system identification approach. A comprehensive data driven model using an extended Kohonen self-organizing map (KSOM) has been developed whose input is a 26 dimensional facial geometric feature vector comprising eye, lip and eyebrow feature points. The analytical face model using this 26 dimensional geometric feature vector has been effectively used to describe the facial changes due to different expressions. This paper thus includes an automated generation scheme of this geometric facial feature vector. The proposed non-heuristic model has been developed using training data from MMI facial expression database. The emotion recognition accuracy of the proposed scheme has been compared with radial basis function network, multi-layered perceptron model and support vector machine based recognition schemes. The experimental results show that the proposed model is very efficient in recognizing six basic emotions while ensuring significant increase in average classification accuracy over radial basis function and multi-layered perceptron. It also shows that the average recognition rate of the proposed method is comparatively better than multi-class support vector machine.