OpenGL programming guide (2nd ed.): the official guide to learning OpenGL version 1.1.
OpenGL programming guide (2nd ed.): the official guide to learning OpenGL version 1.1.
Coding, Analysis, Interpretation, and Recognition of Facial Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Analysis of Facial Expressions: The State of the Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Facial expression recognition from video sequences: temporal and static modeling
Computer Vision and Image Understanding - Special issue on Face recognition
Handbook of Face Recognition
Fully Automatic Facial Action Recognition in Spontaneous Behavior
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Fully Automatic Facial Action Unit Detection and Temporal Analysis
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
A Stereo and Color-based Method for Face Pose Estimation and Facial Feature Extraction
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Human expression recognition from motion using a radial basis function network architecture
IEEE Transactions on Neural Networks
Geometric and Optical Flow Based Method for Facial Expression Recognition in Color Image Sequences
ICCVG 2008 Proceedings of the International Conference on Computer Vision and Graphics: Revised Papers
A new multi-camera based facial expression analysis concept
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part II
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In this paper we present a user independent real-time capable automatic method for recognition of facial expressions related to basic emotions from stereo image sequences. The method automatically detects faces in unconstraint pose based on depth and color information. In order to overcome difficulties caused by increasing change in pose, lighting transitions, or complicated background, we introduce a face normalization algorithm based on an Iterative Closest Point algorithm. In normalized face images we defined a set of physiologically motivated face regions related to a subset of facial muscles which are apt to automatically detect the six well-known basis emotions. Visual facial expression analysis takes place by an optical flow based feature extraction and a nearest neighbor classification, which uses a distance measure, i.e. the current flow vector pattern is matched against empirically determined ground truth data.