Name
Abstract | ||
|---|---|---|
Facial expressions are a natural way to communicate emotional states and intentions. In recent years, automatic facial expression recognition (FER) has been studied due to its practical importance in many human-behavior analysis tasks such as interviews, autonomous-driving, medical treatment, among others. In this paper we propose a method for facial expression recognition based on features extracted with convolutional neural networks (CNN), taking advantage of a pre-trained model in similar tasks. Unlike other approaches, the proposed FER method learns from mixed instances taken from different databases with the goal of improving generalization, a major issue in machine learning. Experimental results show that the FER method is able to recognize the six universal expressions with an accuracy above 92% considering five of the widely used databases. In addition, we have extended our method to deal with micro-expressions recognition (MER). In this regard, we propose three strategies to create a temporal-aggregated feature vector: mean, standard deviation and early fusion. In this case, the best result is 78.80% accuracy. Furthermore, we present a prototype system that implements the two proposed methods for FER and MER as a tool that allows to analyze videos. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s11042-020-08681-4 | Multimedia Tools and Applications |
Keywords | DocType | Volume |
Facial expression recognition, Facial micro-expression recognition, Convolutional neural networks, Machine learning | Journal | 79 |
Issue | ISSN | Citations |
19 | 1380-7501 | 1 |
PageRank | References | Authors |
0.36 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sonia M. González-Lozoya | 1 | 1 | 0.36 |
| Jorge De La Calleja | 2 | 18 | 10.49 |
| Luis Pellegrin | 3 | 1 | 0.36 |
| H. Jair Escalante | 4 | 12 | 2.95 |
| Ma. Auxilio Medina | 5 | 17 | 6.69 |
| Antonio Benitez-Ruiz | 6 | 1 | 0.36 |