Name
Abstract | ||
|---|---|---|
Automated measurement of the intensity of spontaneous facial Action Units (AU) defined by the Facial Action Coding System (FACS) in video sequences is a challenging problem. This paper proposes a person-adaptive methodology for the intensity estimation of spontaneous AUs. We formulate this problem as a source separation problem where we consider the observed AUs as the source signals to be separated from each other and other information given by a sequence of facial images. We first compute an initial estimation of the sources, called observations, using sparse linear regression functions. We then develop and apply a Bayesian source separation method that recruits the prior information of the sources to iteratively improve the initial estimations/observations in an adaptive fashion. Furthermore, our approach adaptively uses some testing information (but not the ground-truth labels) to improve the performance of the approach (i.e., Person-Adaptive model). Our experimental results on DISFA, UNBC-McMaster and FERA2015 databases show that this approach is very promising for automated measurement of the intensity of spontaneous facial AUs. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TAFFC.2017.2707484 | IEEE Transactions on Affective Computing |
Keywords | Field | DocType |
Gold,Estimation,Source separation,Bayes methods,Databases,Encoding,Linear regression | Facial Action Coding System,Pattern recognition,Psychology,Artificial intelligence,Source separation,Bayesian probability,Linear regression | Journal |
Volume | Issue | ISSN |
10 | 2 | 1949-3045 |
Citations | PageRank | References |
0 | 0.34 | 15 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohammad Reza Mohammadi | 1 | 26 | 6.71 |
| Emad Fatemizadeh | 2 | 117 | 13.86 |
| Mohammad H. Mahoor | 3 | 861 | 55.59 |