Earticle

초록

영어

Facial expression recognition, as one of the important topics in pattern recognition and computer vision, has broad applications in fields of human-computer interaction, psychological behavior analysis, image understanding. This paper presents a novel facial expression recognition method based on global and local features extraction and facial recognition using decision-level fusion. We first extract Local Directional Pattern (LDP) global features of the whole face which can guarantee basic expression difference and decrease the influence of no-facial region meanwhile, and then the Local Directional Pattern Variance (LDPv) descriptor is used to extract local features of regions of eyes and mouth to extrude their contribution on expression changes. After feature extraction, PCA technique is utilized to reduce dimension of input feature space. Finally, in order to avoid redundant feature repeat we don't use feature fusion with simple concatenation, a decision-level fusion for global LDP feature and local LDPv feature by Support Vector Machine (SVM) is selected to recognition respectively. Furthermore, we also research the optimal parameters for regions-dividing and weight of LDPv. The proposed method is investigated on two standard databases Cohn-Kanade and JAFFE, and extensive experimental results indicate the effectiveness.

목차

Abstract
 1. Introduction
 2. Facial Feature Extraction
  2.1 LDP and global feature extraction
  2.2. LDPv and local feature extraction
 3. Feature dimensionality reduction and expression recognition
  3.1. Feature dimensionality reduction using PCA
  3.2. SVM and expression recognition
 4. Experimental results and analysis
  4.1. Databases and experiment setup
  4.2. Results and analysis
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Juxiang Zhou [ Key Laboratory of Education Informalization for Nationalities, Ministry of Education, Yunnan Normal University, Kunming, China ]
• Tianwei Xu [ Key Laboratory of Education Informalization for Nationalities, Ministry of Education, Yunnan Normal University, Kunming, China, 2College of Information, Yunnan Normal University, Kunming, China ]
• Jianhou Gan [ Key Laboratory of Education Informalization for Nationalities, Ministry of Education, Yunnan Normal University, Kunming, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Bio-Science and Bio-Technology

• 간기

  격월간

• pISSN

  2233-7849

• 수록기간

  2009~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605