TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Multi-classifier Fusion Based Facial Expression Recognition Approach |
| 영문제목(English Title) |
Multi-classifier Fusion Based Facial Expression Recognition Approach |
| 저자(Author) |
Xibin Jia
Yanhua Zhang
David Powers
Humayra Binte Ali
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| 원문수록처(Citation) |
VOL 08 NO. 01 PP. 0196 ~ 0212 (2014. 01) |
한글내용
(Korean Abstract) |
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영문내용
(English 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.
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| 키워드(Keyword) |
Multi-classifier fusion
stacking
facial expression recognition
kappa-error diagram
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| 파일첨부 |
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