We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Multi-classifier Fusion Based Facial Expression Recognition Approach.
- Authors
Xibin Jia; Yanhua Zhang; Powers, David; Ali, Humayra Binte
- Abstract
Facial expression recognition is an important part in emotional interaction between human and machine. This paper proposes a facial expression recognition approach based on multi-classifier fusion with stacking algorithm. The kappa-error diagram is employed in base-level classifiers selection, which gains insights about which individual classifier has the better recognition performance and how diverse among them to help improve the recognition accuracy rate by fusing the complementary functions. In order to avoid the influence of the chance factor caused by guessing in algorithm evaluation and get more reliable awareness of algorithm performance, kappa and informedness besides accuracy are utilized as measure criteria in the comparison experiments. To verify the effectiveness of our approach, two public databases are used in the experiments. The experiment results show that compared with individual classifier and two other typical ensemble methods, our proposed stacked ensemble system does recognize facial expression more accurately with less standard deviation. It overcomes the individual classifier's bias and achieves more reliable recognition results.
- Subjects
HUMAN facial recognition software; HUMAN-machine systems; DATA fusion (Statistics); STATISTICAL ensembles; ALGORITHMS
- Publication
KSII Transactions on Internet & Information Systems, 2014, Vol 8, Issue 1, p196
- ISSN
1976-7277
- Publication type
Article
- DOI
10.3837/tiis.2014.01.012