Active shape models—their training and application
Computer Vision and Image Understanding
Automatic Interpretation and Coding of Face Images Using Flexible Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Feature-Point Tracking by Optical Flow Discriminates Subtle Differences in Facial Expression
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Handwritten Recognition with Multiple Classifiers for Restricted Lexicon
SIBGRAPI '04 Proceedings of the Computer Graphics and Image Processing, XVII Brazilian Symposium
Evaluation of Face Resolution for Expression Analysis
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 5 - Volume 05
Robust Pose Invariant Facial Feature Detection and Tracking in Real-Time
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Multiview Facial Feature Tracking with a Multi-modal Probabilistic Model
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Static topographic modeling for facial expression recognition and analysis
Computer Vision and Image Understanding
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This work reports a study about the use of Gabor coefficients and coordinates of fiducial (landmark) points to represent facial features and allow the discrimination between photogenic and non-photogenic facial images, using neural networks. Experiments have been performed using 416 images from the Cohn-Kanade AU-Coded Facial Expression Database [1]. In order to extract fiducial points and classify the expressions, a manual processing was performed. The facial expression classifications were obtained with the help of the Action Unit information available in the image database. Various combinations of features were tested and evaluated. The best results were obtained with a weighted sum of a neural network classifier using Gabor coefficients and another using only the fiducial points. These indicated that fiducial points are a very promising feature for the classification performed.