Authors:
Luana Bezerra Batista
;
Herman Martins Gomes
and
João Marques de Carvalho
Affiliation:
Universidade Federal de Campina Grande, Brazil
Keyword(s):
Facial Expression Recognition, Photogeny, Principal Component Analysis, Multi-Layer Perceptron.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Computer Vision, Visualization and Computer Graphics
;
Data Manipulation
;
Feature Extraction
;
Features Extraction
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Image and Video Analysis
;
Informatics in Control, Automation and Robotics
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Signal Processing, Sensors, Systems Modeling and Control
;
Soft Computing
;
Theory and Methods
Abstract:
Facial Expression Recognition Systems (FERS) are usually applied to human-machine interfaces, enabling services that require identification of the emotional state of the user. This paper presents a new approach to the facial expression recognition problem, by addressing the question of whether or not it is possible to classify previously labeled photogenic and non-photogenic face images, based on their appearance. A Multi-Layer Perceptron (MLP) is trained with PCA representations of the face images to learn the relationships between facial expressions and the concept of a good photography of the face of a person. In the experiments, the generalization performances using MLP and Support Vector Machines (SVM) were analyzed. The results have shown that Principal Component Analysis (PCA) combined with MLP represent a promising approach to the problem.