Automatic Analysis of Facial Expressions: The State of the Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
Online Facial Expression Recognition Based on Personalized Galleries
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Comprehensive Database for Facial Expression Analysis
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Constrained Phase---Based Personalized Facial Feature Tracking
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Personalized Facial Expression Recognition in Color Image
ICICIC '09 Proceedings of the 2009 Fourth International Conference on Innovative Computing, Information and Control
Estimating face pose by facial asymmetry and geometry
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
| Hi-index | 0.00 |
Automatic facial expression analysis is the most commonly studied aspect of behavior understanding and human-computer interface. Most facial expression recognition systems are implemented with general expression models. However, the same facial expression may vary differently across humans, this can be true even for the same person when the expression is displayed in different contexts. These factors present a significant challenge for recognition. To cope with this problem, we present in this paper a personalized facial action recognition framework that we wish to use in a clinical setting with familiar faces; in this case a high accuracy level is required. The graph fitting method that we are using offers a constrained tracking approach on both shape (using procrustes transformation) and appearance (using weighted Gabor wavelet similarity measure). The tracking process is based on a modified Gabor-phase based disparity estimation technique. Experimental results show that the facial feature points can be tracked with sufficient precision leading to a high facial expression recognition performance.