基于光流方向信息熵統計的微表情捕捉

李丹; 解侖; 盧婷; 韓晶; 胡波; 王志良; 任福繼

doi:10.13374/j.issn2095-9389.2017.11.016

基于光流方向信息熵統計的微表情捕捉

doi: 10.13374/j.issn2095-9389.2017.11.016

李丹¹,
解侖^1, ,,
盧婷¹,
韓晶¹,
胡波¹,
王志良¹,
任福繼²

1) 北京科技大學計算機與通信工程學院, 北京 100083
2) 合肥工業大學計算機與信息學院, 合肥 230009

基金項目:

國家自然科學基金資助項目(61672093,61432004)

國家重點研發計劃重點專項課題資助項目(2016YFB1001404)

詳細信息

中圖分類號: TP751.1
計量
- 文章訪問數: 656
- HTML全文瀏覽量: 192
- PDF下載量: 20
- 被引次數: 0
出版歷程
- 收稿日期: 2016-12-15

Capture of microexpressions based on the entropy of oriented optical flow

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

摘要

摘要: 以光流法為依據,提出了一種基于光流方向信息熵(entropy of oriented optical flow,EOF)統計的方法捕捉微表情關鍵幀.首先,采用改進的Horn-Schunck光流法提取視頻流中相鄰兩幀圖像的微表情運動特征;其次,采用閾值分析法篩選出投影速度模值較大的光流向量;之后,采用圖像信息熵統計光流變化角度,進而得到視頻序列的方向信息熵向量,通過對熵向量的分析,實現微表情關鍵幀捕捉;最后,本實驗采用芬蘭奧盧大學的SMIC微表情數據庫和中國科學院心理研究所傅小蘭的CASME微表情數據庫作為實驗樣本,通過與傳統的幀差法比較,證明了本文提出的算法優于幀差法,能夠較好地表現出微表情變化趨勢,為微表情識別提供基礎.
- 微表情 /
- H-S光流 /
- 方向信息熵 /
- 幀差法 /
- 關鍵幀捕捉
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

HTML全文

參考文獻(13)

[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