Loading...

An adaptive Bayesian source separation method for intensity estimation of facial aus

Mohammadi, M. R ; Sharif University of Technology | 2019

469 Viewed
  1. Type of Document: Article
  2. DOI: 10.1109/TAFFC.2017.2707484
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2019
  4. Abstract:
  5. 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
  6. Keywords:
  7. Bayesian method ; Facial action units ; Intensity measurement ; Bayesian networks ; Computer keyboards ; Iterative methods ; Separation ; Adaptive methodologies ; Bayesian methods ; Bayesian source separations ; Facial action ; Facial Action Coding System ; Intensity measurements ; Sparse linear regressions ; Sparse regression ; Source separation
  8. Source: IEEE Transactions on Affective Computing ; Volume 10, Issue 2 , 2019 , Pages 144-154 ; 19493045 (ISSN)
  9. URL: https://ieeexplore.ieee.org/document/7933209