Real-time 2D+3D facial action and expression recognition
Pattern Recognition
Coupled Gaussian process regression for pose-invariant facial expression recognition
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
3D Facial expression recognition based on histograms of surface differential quantities
ACIVS'11 Proceedings of the 13th international conference on Advanced concepts for intelligent vision systems
3D corpus of spontaneous complex mental states
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part I
Interpreting hand-over-face gestures
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part II
Static and dynamic 3D facial expression recognition: A comprehensive survey
Image and Vision Computing
3D/4D facial expression analysis: An advanced annotated face model approach
Image and Vision Computing
Recognition of 3D facial expression dynamics
Image and Vision Computing
3D shape estimation in video sequences provides high precision evaluation of facial expressions
Image and Vision Computing
Real-time expression recognition from dynamic sequences of 3D facial scans
EG 3DOR'12 Proceedings of the 5th Eurographics conference on 3D Object Retrieval
A review of motion analysis methods for human Nonverbal Communication Computing
Image and Vision Computing
Automatic facial expression recognition in real-time from dynamic sequences of 3D face scans
The Visual Computer: International Journal of Computer Graphics
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Traditionally, facial expression recognition (FER) issues have been studied mostly based on modalities of 2D images, 2D videos, and 3D static models. In this paper, we propose a spatio-temporal expression analysis approach based on a new modality, 3D dynamic geometric facial model sequences, to tackle the FER problems. Our approach integrates a 3D facial surface descriptor and Hidden Markov Models (HMM) to recognize facial expressions. To study the dynamics of 3D dynamic models for FER, we investigated three types of HMMs: temporal 1D-HMM, pseudo 2D-HMM (a combination of a spatial HMM and a temporal HMM), and real 2D-HMM. We also created a new dynamic 3D facial expression database for the research community. The results show that our approach achieves a 90.44% person-independent recognition rate for distinguishing six prototypic facial expressions. The advantage of our method is demonstrated as compared to methods based on 2D texture images, 2D/3D Motion Units, and 3D static range models. Further experimental evaluations also verify the benefits of our approach with respect to partial facial surface occlusion, expression intensity changes, and 3D model resolution variations.