A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Classification of Single Facial Images
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
Locating Facial Features in Image Sequences using Neural Networks
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Hierarchical Wavelet Networks for Facial Feature Localization
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Face Recognition Based on Fitting a 3D Morphable Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-time View-based Face Alignment using Active Wavelet Networks
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Deformation Analysis for 3D Face Matching
WACV-MOTION '05 Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1 - Volume 01
A 3D Facial Expression Database For Facial Behavior Research
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
A survey of approaches and challenges in 3D and multi-modal 3D + 2D face recognition
Computer Vision and Image Understanding
3D Facial Expression Recognition Based on Primitive Surface Feature Distribution
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Three-Dimensional Face Recognition Using Shapes of Facial Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
3D shape constraint for facial feature localization using probabilistic-like output
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Bilinear Models for 3-D Face and Facial Expression Recognition
IEEE Transactions on Information Forensics and Security
IEEE Transactions on Image Processing
Expression-Invariant Representations of Faces
IEEE Transactions on Image Processing
Facial expression recognition using 3D facial feature distances
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
Automatic edge-based localization of facial features from images with complex facial expressions
Pattern Recognition Letters
2D representation of facial surfaces for multi-pose 3D face recognition
Pattern Recognition Letters
Automatic facial expression recognition based on spatiotemporal descriptors
Pattern Recognition Letters
3D/4D facial expression analysis: An advanced annotated face model approach
Image and Vision Computing
Person independent 3D facial expression recognition by a selected ensemble of SIFT descriptors
EG 3DOR'10 Proceedings of the 3rd Eurographics conference on 3D Object Retrieval
Multi-pose 3D face recognition based on 2D sparse representation
Journal of Visual Communication and Image Representation
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We propose a novel approach to human facial expression recognition using only the shape information at a finite set of fiducial points, extracted from the 3D neutral and expressive faces. In the course of applying the technique to the facial database, BU-3DFE, which contains facial shape and 2D color (''texture'') information, we extract from the images of neutral and expressive faces, salient contours in the facial interest-regions around the eyebrows, eyes, nose and mouth by invoking an active contour algorithm. The contours are then uniformly sampled and mapped onto the 3D-mesh dataset in order to generate a shape (and color) description of the interest-regions. By a matrix-algebraic operation on the shape of the neutral and expressive faces, a shape feature-matrix is computed for each expression and for each person, which is then subjected to the proposed modified PCA approach to recognize expressions. Classification results are presented to demonstrate the effectiveness of the proposed approach. It is also found that accuracy estimates compare favorably with those in the literature on facial expression recognition from 3D-mesh datasets.