Capture of microexpressions based on the entropy of oriented optical flow

LI Dan; XIE Lun; LU Ting; HAN Jing; HU Bo; WANG Zhi-liang; REN Fu-ji

doi:10.13374/j.issn2095-9389.2017.11.016

Volume 39 Issue 11

Nov. 2017

Turn off MathJax

Article Contents

Article Navigation > Chinese Journal of Engineering > 2017 > 39(11): 1727-1734

LI Dan, XIE Lun, LU Ting, HAN Jing, HU Bo, WANG Zhi-liang, REN Fu-ji. Capture of microexpressions based on the entropy of oriented optical flow[J]. Chinese Journal of Engineering, 2017, 39(11): 1727-1734. doi: 10.13374/j.issn2095-9389.2017.11.016

Citation:

LI Dan, XIE Lun, LU Ting, HAN Jing, HU Bo, WANG Zhi-liang, REN Fu-ji. Capture of microexpressions based on the entropy of oriented optical flow[J]. Chinese Journal of Engineering, 2017, 39(11): 1727-1734. doi: 10.13374/j.issn2095-9389.2017.11.016

Citation:

PDF( 667 KB)

Capture of microexpressions based on the entropy of oriented optical flow

doi: 10.13374/j.issn2095-9389.2017.11.016

LI Dan¹,
XIE Lun^{1
,
,},
LU Ting¹,
HAN Jing¹,
HU Bo¹,
WANG Zhi-liang¹,
REN Fu-ji²

1) School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
2) School of Computer and Information, Hefei University of Technology, Hefei 230009, China

Received Date: 2016-12-15

Abstract

Abstract

This paper proposes an algorithm that is effective in detecting the key frame of microexpression based on the entropy of oriented optical flow. Initially, this paper used an improved Horn-Schunck optical flow to extract the motion features of adjacent frames. Then, the threshold algorithm was used to filter the optical flow vectors with high-projective modulus. To capture the key frame of microexpression, the paper used information entropy to count the direction of optical flow vectors and analyzed the changing of microexpressions using an entropy vector of video sequences. Finally, the algorithm in this paper was verified with microexpression database SMIC (Oulu University) and CASME (the Director of the Institute of Psychology at the Chinese Academy of Sciences, Fu Xiaolan). Compared with traditional frame differences, experiments show that the algorithm is good not only in expressing the trend of the microexpression but also in providing the basis for microexpression recognition.
- microexpression,
- Horn-Schunck optical flow,
- oriented optical flow,
- frame difference,
- key-frame capture

FullText(HTML)

References(13)

References

[2]	Ekman P, Friesen W V. Nonverbal leakage and clues to deception. Psychiatry-interpersonal Biol Processes, 1969, 32(1):88
[3]	Porter S, Ten Brinke L. Reading between the lies:Identifying concealed and falsified emotions in universal facial expressions. Psychological Sci, 2008, 19(5):508
[4]	Patel D, Zhao G Y, Pietikäinen M. Spatiotemporal integration of optical flow vectors for microexpression detection//International Conference on Advanced Concepts for Intelligent Vision Systems. Catania, 2015:369
[5]	Polikovsky S, Kameda Y, Ohta Y. Facial micro-expressions recognition using high speed camera and 3D-gradients descriptor//Proceedings of 3rd International Conference on Imaging for Crime Detection and Prevention. London, 2009:16
[6]	Yao S Q, He N, Zhang H Q, et al. Micro-expression recognition by feature points tracking//10th International Conference on Communications. Bucharest, 2014:1
[8]	Wu Q, Shen X B, Fu X L. The machine knows what you are hiding:an automatic micro-expression recognition system//Affective Computing and Intelligent Interaction. Memphis, 2011:152
[9]	Guo Y J, Tian Y T, Gao X, et al. Micro-expression recognition based on local binary patterns from three orthogonal planes and nearest neighbor method//International Joint Conference on Neural Networks. Beijing, 2014:3473
[10]	Zhu S H, Hu J J, Shi Z. Local abnormal behavior detection based on optical flow and spatio-temporal gradient. Multimedia Tools Appl, 2016, 75(15):9445
[11]	Siddiqi M H, Ali R, Khan A M, et al. Facial expression recognition using active contour-based face detection, facial movement-based feature extraction, and non-linear feature selection. Multimedia Syst, 2015, 21(6):541
[12]	Pfister T, Li X B, Zhao G Y, et al. Differentiating spontaneous from posed facial expressions within a generic facial expression recognition framework//2011 IEEE International Conference on Computer Vision Workshops. Barcelona, 2011:868
[13]	Liu X, Cheung Y M, Tang Y Y. Lip event detection using oriented histograms of regional optical flow and low rank affinity pursuit. Comput Vision Image Understanding, 2016, 148:153
[15]	Liu K H, Liu T J, Liu H H, et al. Facial makeup detection via selected gradient orientation of entropy information//2015 IEEE International Conference on Image Processing. Quebec City, 2015:4067
[16]	Pfister T, Li X B, Zhao G Y, et al. Recognizing spontaneous facial micro-expressions//2011 IEEE International Conference on International Conference on Computer Vision. Barcelona, 2011:1449