Recognizing Action Units for Facial Expression Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hybrid HMM/ANN Systems for Speech Recognition: Overview and New Research Directions
Adaptive Processing of Sequences and Data Structures, International Summer School on Neural Networks, "E.R. Caianiello"-Tutorial Lectures
Comprehensive Database for Facial Expression Analysis
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Handbook of Face Recognition
Fully Automatic Facial Action Unit Detection and Temporal Analysis
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Spontaneous vs. posed facial behavior: automatic analysis of brow actions
Proceedings of the 8th international conference on Multimodal interfaces
Foundations of human computing: facial expression and emotion
Proceedings of the 8th international conference on Multimodal interfaces
A generative framework for real time object detection and classification
Computer Vision and Image Understanding - Special issue on eye detection and tracking
Particle filtering with factorized likelihoods for tracking facial features
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Emotionally aware automated portrait painting
Proceedings of the 3rd international conference on Digital Interactive Media in Entertainment and Arts
Audiovisual laughter detection based on temporal features
ICMI '08 Proceedings of the 10th international conference on Multimodal interfaces
Modelling human perception of static facial expressions
Image and Vision Computing
Human-computer intelligent interaction: a survey
HCI'07 Proceedings of the 2007 IEEE international conference on Human-computer interaction
Products appreciation by facial expressions analysis
Proceedings of the 11th International Conference on Computer Systems and Technologies and Workshop for PhD Students in Computing on International Conference on Computer Systems and Technologies
Fast-FACS: a computer-assisted system to increase speed and reliability of manual FACS coding
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part I
Context-independent facial action unit recognition using shape and gabor phase information
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part I
Analysis of landmarks in recognition of face expressions
Pattern Recognition and Image Analysis
Recognition of 3D facial expression dynamics
Image and Vision Computing
ICEC'12 Proceedings of the 11th international conference on Entertainment Computing
A dynamic approach for detecting naturalistic affective states from facial videos during HCI
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Proceedings of the 2nd Workshop on Machine Learning for Interactive Systems: Bridging the Gap Between Perception, Action and Communication
Nearest neighbor weighted average customization for modeling faces
Machine Vision and Applications
| Hi-index | 0.01 |
The analysis of facial expression temporal dynamics is of great importance for many real-world applications. Being able to automatically analyse facial muscle actions (Action Units, AUs) in terms of recognising their neutral, onset, apex and offset phases would greatly benefit application areas as diverse as medicine, gaming and security. The base system in this paper uses Support Vector Machines (SVMs) and a set of simple geometrical features derived from automatically detected and tracked facial feature point data to segment a facial action into its temporal phases. We propose here two methods to improve on this base system in terms of classification accuracy. The first technique describes the original time-independent set of features over a period of time using polynomial parametrisation. The second technique replaces the SVM with a hybrid SVM/Hidden Markov Model (HMM) classifier to model time in the classifier. Our results show that both techniques contribute to an improved classification accuracy. Modeling the temporal dynamics by the hybrid SVM-HMM classifier attained a statistically significant increase of recall and precision by 4.5% and 7.0%, respectively.