Automatic Analysis of Facial Expressions: The State of the Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
Connected Vibrations: A Modal Analysis Approach for Non-Rigid Motion Tracking
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Comprehensive Database for Facial Expression Analysis
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Facial expression recognition from video sequences: temporal and static modeling
Computer Vision and Image Understanding - Special issue on Face recognition
Facial Action Unit Recognition by Exploiting Their Dynamic and Semantic Relationships
IEEE Transactions on Pattern Analysis and Machine Intelligence
How to distinguish posed from spontaneous smiles using geometric features
Proceedings of the 9th international conference on Multimodal interfaces
A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multimodal coordination of facial action, head rotation, and eye motion during spontaneous smiles
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Are you really smiling at me? spontaneous versus posed enjoyment smiles
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
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Automatic detection of spontaneous versus posed facial expressions received a lot of attention in recent years. However, almost all published work in this area use complex facial features or multiple modalities, such as head pose and body movements with facial features. Besides, the results of these studies are not given on public databases. In this paper, we focus on eyelid movements to classify spontaneous versus posed smiles and propose distance-based and angular features for eyelid movements. We assess the reliability of these features with continuous HMM, k-NN and naive Bayes classifiers on two different public datasets. Experimentation shows that our system provides classification rates up to 91 per cent for posed smiles and up to 80 per cent for spontaneous smiles by using only eyelid movements. We additionally compare the discrimination power of movement features from different facial regions for the same task.