Unlabeled facial expression capture method in virtual reality system based on big data

被引：0
作者：
Gao F. [1 ,2 ]
机构：
[1] School of Artificial Intelligence, Chongqing University of Arts and Sciences, Chongqing
[2] Lyceum of Philippines University-Batangas, Batangas City
来源：
International Journal of Information and Communication Technology | 2021年 / 18卷 / 03期
关键词：
Big data; Facial expression capture; Facial feature points; Unlabeled; Virtual reality system;
D O I：
10.1504/ijict.2021.10037614
中图分类号：
学科分类号：
摘要：
In view of the problems of high error rate and low efficiency in the traditional method of facial expression capture without markers, a method of facial expression capture without markers based on large amount of data is proposed. Haar feature is used to determine the initial position of human face, and active shape model is used to extract unmarked facial feature points. The extracted feature points and the generated triangle mesh are tracked by the optical flow tracking method. The displacement of the face feature points is used to promote the overall change of the mesh and complete the unmarked facial expression capture. The experimental results show that the error rate of this method is in the range of 1.2%-1.7%, the error rate is small, and it needs 20 s-34 s to capture facial expression, which is more practical and efficient. © 2021 Inderscience Enterprises Ltd.. All rights reserved.
引用
收藏
页码：261 / 274
页数：13
相关论文
共 24 条
  • [1] Cai C.L., Zhang Y.B., A three-dimensional face recognition method for anti-attitude and expression changes, Applied Optics, 39, 4, pp. 53-61, (2018)
  • [2] Cao W.Q., Shi M., Similar face search based on face recognition mode, Journal of Image and Graphics, 21, 10, pp. 1365-1375, (2018)
  • [3] Cao Z.Y., Zhang J.W., Research on face restoration algorithm based on semi-supervised learning to generate confrontation networks, Journal of Electronics & Information Technology, 40, 2, pp. 323-330, (2018)
  • [4] Chang P.P., Mei J., Facial contour 3D face recognition with robust expression, Chinese Journal of Image and Graphics, 20, 3, pp. 332-339, (2018)
  • [5] Dong X.L., Lu Y.X., Stylized portrait generation algorithm using triangular coordinate system, Journal of Xi'an Jiaotong University, 52, 4, pp. 139-144, (2018)
  • [6] Fu Z.H., Li T.R., Two-dimensional and three-dimensional multi-mode face database construction, Data Acquisition and Processing, 32, 3, pp. 595-603, (2017)
  • [7] Hu M., Huang Z., Facial expression recognition based on regional NBPR features and credibility correction, Computer Science and Exploration, 11, 3, pp. 459-467, (2017)
  • [8] Hu Z.P., Sun Z., Multi-level deep network fusion face recognition algorithm, Pattern Recognition and Artificial Intelligence, 30, 5, pp. 448-455, (2017)
  • [9] Hua S.S., Face image recognition based on Zernike feature extraction and LDA, Control Engineering, 25, 2, pp. 198-203, (2018)
  • [10] Jiang C.Z., Su J.B., Expression recognition based on sparse feature selection and probabilistic linear discriminant analysis, Chinese Journal of Electronics, 46, 7, pp. 1710-1718, (2018)
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