Facial expression recognition based on the binary code of edges

被引:0
作者
Feng, Xiaoyi [1 ]
Lai, Yangming [1 ]
Wang, Wenxing [2 ]
Cui, Shaoxing [3 ]
Peng, Jinye [3 ]
Jiang, Xiaoyu [1 ]
机构
[1] School of Electronics and Information, Northwestern Polytechnical Univ., Xi'an
[2] AVIC Aeronautical Science and Technology Key Lab. of Flight Simulation, CFTE, Xi'an
[3] School of Information Science and Technology, Northwest Univ., Xi'an
来源
Xi'an Dianzi Keji Daxue Xuebao/Journal of Xidian University | 2015年 / 42卷 / 03期
关键词
Binary images; Edge detection; Facial expression recognition; Feature extraction;
D O I
10.3969/j.issn.1001-2400.2015.03.031
中图分类号
学科分类号
摘要
Facial expression recognition mainly involves face locating, feature extraction, and expression classification. However, extracting effective features of facial expressions from original face images is a vital step for improving facial expression recognition. In this paper, a binary code of edge method is proposed for extracting the expression feature, which is used for facial expression recognition. Firstly, the face image is processed by an edge detection operator. Then, the proposed binary code method is applied to describe the edge binary image as the expression feature. Finally, the SVM classifier is used for facial expression recognition. Experiments are performed on JAFFE facial expression database, and the results show that the proposed method achieves a higher recognition rate than the traditional method. ©, 2015, Science Press. All right reserved.
引用
收藏
页码:186 / 191
页数:5
相关论文
共 16 条
  • [1] Youssif A.A.A., Asker W.A.A., Automatic Facial Expression Recognition System Based on Geometric and Appearance Features, Computer and Information Science, 4, 2, pp. 115-124, (2011)
  • [2] Liu W., Song C., Wang Y., Et al., Facial Expression Recognition Based on Gabor Features and Sparse Representation, IEEE Proceedings of 12 International Conference on Control, Automation, Robotics & Vision, pp. 1402-1406, (2012)
  • [3] Banerji S., Sinha A., Liu C., New Image Descriptors Based on Color, Texture, Shape and Wavelets for Object and Scene Image Classification, Neurocomputing, 117, pp. 173-185, (2013)
  • [4] Zalewski L., Gong S., Synthesis and Recognition of Facial Expressions in Virtual 3D Views, IEEE International Conference on Automatic Face and Gesture Recognition, pp. 493-498, (2004)
  • [5] Shrinivasa N.C.L., Shekhar J.S., Das P.K., Et al., Automatic Facial Expression Recognition Using Extended AR-LBP, Proceedings of 6th International Conference on Information Processing, pp. 244-252, (2012)
  • [6] Zhang S., Zhao X., Lei B., Facial Expression Recognition Based on Local Binary Patterns and Local Fisher Discriminant Analysis, Wseas Transactions on Signal Processing, 8, 1, pp. 21-31, (2012)
  • [7] Shrinivasa N.C.L., Das P.K., Nair S.B., Asymmetric Region Local Binary Pattern Operator for Person-dependent Facial Expression Recognition, Proceedings of 2012 International Conference on Computing, Communication and Applications, pp. 1-5, (2012)
  • [8] Chellappa R., Wilson C.L., Sirohey S., Human and Machine Recognition of Faces: a Survey, Proceedings of the IEEE, 83, 5, pp. 705-741, (1995)
  • [9] Ekman P., Friesen W.V., Hager J.C., Facial Action Coding System, (2002)
  • [10] Zhao H., Wang Z., Liu Y., A Survey of Automatic Facial Action Units Recognition, Journal of Computer Aided-Design & Computer Graphics, 22, 5, pp. 894-903, (2010)