Name
Abstract | ||
|---|---|---|
Facial expression recognition has been an active research area in the past ten years, with a growing application area like avatar animation and neuromarketing. The recognition of facial expressions is not an easy problem for machine learning methods, since different people can vary in the way that they show their expressions. And even an image of the same person in one expression can vary in brightness, background and position. Therefore, facial expression recognition is still a challenging problem in computer vision. In this work, we propose a simple solution for facial expression recognition that uses a combination of standard methods, like Convolutional Network and specific image pre-processing steps. Convolutional networks, and the most machine learning methods, achieve better accuracy depending on a given feature set. Therefore, a study of some image pre-processing operations that extract only expression specific features of a face image is also presented. The experiments were carried out using a largely used public database for this problem. A study of the impact of each image pre-processing operation in the accuracy rate is presented. To the best of our knowledge, our method achieves the best result in the literature, 97.81% of accuracy, and takes less time to train than state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/SIBGRAPI.2015.14 | Brazilian Symposium on Computer Graphics and Image Processing |
Keywords | Field | DocType |
Facial Expression,Convolutional Networks,Computer Vision,Machine Learning,Expression Specific Features | Neuromarketing,Computer vision,Pattern recognition,Expression (mathematics),Three-dimensional face recognition,Facial expression recognition,Computer science,Feature (computer vision),Feature extraction,Facial expression,Feature (machine learning),Artificial intelligence | Conference |
ISSN | Citations | PageRank |
1530-1834 | 13 | 0.67 |
References | Authors | |
33 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andre Teixeira Lopes | 1 | 17 | 1.44 |
| Edilson De Aguiar | 2 | 514 | 25.53 |
| Thiago Oliveira-Santos | 3 | 84 | 4.11 |