Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Example-Based Learning for View-Based Human Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Classification of Single Facial Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
Online Facial Expression Recognition Based on Personalized Galleries
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
A General Framework for Object Detection
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Robust Real-Time Face Detection
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Overview of the Face Recognition Grand Challenge
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
3D facial image recognition using a nose volume and curvature based eigenface
GMP'06 Proceedings of the 4th international conference on Geometric Modeling and Processing
The new italian audio and video emotional database
COST'09 Proceedings of the Second international conference on Development of Multimodal Interfaces: active Listening and Synchrony
On speech and gestures synchrony
COST'10 Proceedings of the 2010 international conference on Analysis of Verbal and Nonverbal Communication and Enactment
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This work approaches the problem of recognizing emotional facial expressions in static images focusing on three preprocessing techniques for feature extraction such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Gabor filters. These methods are commonly used for face recognition and the novelty consists in combining features provided by them in order to improve the performance of an automatic procedure for recognizing emotional facial expressions. Testing and recognition accuracy were performed on the Japanese Female Facial Expression (JAFFE) database using a Multi-Layer Perceptron (MLP) Neural Network as classifier. The best classification accuracy on variations of facial expressions included in the training set was obtained combining PCA and LDA features (93% of correct recognition rate), whereas, combining PCA, LDA and Gabor filter features the net gave 94% of correct classification on facial expressions of subjects not included in the training set.