Feature extraction from faces using deformable templates
International Journal of Computer Vision
Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Example-Based Learning for View-Based Human Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data mining: concepts and techniques
Data mining: concepts and techniques
Tracking Facial Feature Points with Gabor Wavelets and Shape Models
AVBPA '97 Proceedings of the First International Conference on Audio- and Video-Based Biometric Person Authentication
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Multi-Modal System for Locating Heads and Faces
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Human expression recognition from motion using a radial basis function network architecture
IEEE Transactions on Neural Networks
Face recognition: a convolutional neural-network approach
IEEE Transactions on Neural Networks
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In this paper, we present a real-time method for mining the facial patterns and tracking the facial emotion. We first recognize the facial patterns based on a nonsupervised idea of pattern mining from video. To raise the mining speed, we adopt some simple and rapid filters called Gabor-like filters to mine individual feature patterns. To evaluate the facial patterns, we match deformable templates and analyze the histogram of local region. To track the mouth contour, we model dynamically the color distribution of lip and skin by EM algorithm and segment lip color by the Bayes classification rule. Finally, we represent the facial emotion with an avatar by transferring some parameters. Through experiments, our method can rapidly mine the facial patterns and track different facial expressions of different people whether they wear glasses or not.