Micro-expression Recognition Based on Global Optical Flow Feature
ZHANG Xuange1, TIAN Yantao1,2, YAN Fei1, WANG Meiqian1
1.College of Communication Engineering, Jilin University, Changchun 130025 2.Key Laboratory of Bionic Engineering of Ministry of Education, Jilin University, Changchun 130025
Abstract:The global optical flow feature extraction algorithm based on gradient is studied to improve the effect of micro-expression recognition. To solve the problem of large displacement between fine images, the multi-resolution strategy is introduced to slice the images, and the iterative reweighted least squares method is used to optimize the objective function layer by layer. Thus, the optimal optical flow is obtained, and the accuracy of motion tracking is ensured. To reflect the action differences in key parts of faces, a partition feature statistic method is proposed. The optical flow image is divided into a number of rectangular regions and in these regions the optical flow motion is concluded. Consequently, the effectiveness of the feature is enhanced. The experimental results show that overall recognition accuracy and discrimination of emotion categories are significantly improved.
[1] ONG S C W, RANGANATH S. Automatic Sign Language Analysis: A Survey and the Future beyond Lexical Meaning. IEEE Trans on Pattern Analysis and Machine Intelligence, 2005, 27(6): 873-891. [2] BHUSHAN B. Study of Facial Micro-expressions in Psychology // MANDAL M K, AWASTHI A, eds. Understanding Facial Expre-ssions in Communication. New Delhi, India: Springer, 2015: 265-286. [3] 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 // Proc of the International Joint Confe-rence on Neural Networks. New York, USA: IEEE, 2014: 3473-3479. [4] 梁 静,颜文靖,吴 奇,等.微表情研究的进展与展望.中国科学基金, 2013(2): 75-78, 82. (LIANG J, YAN W J, WU Q, et al. Recent Advances and Future Trends in Microexpression Research. Bulletin of National Natural Science Foundation of China, 2013(2): 75-78, 82.) [5] POLIKOVSKY S, KAMEDA Y, OHTA Y. Facial Micro-expressions Recognition Using High Speed Camera and 3D-Gradient Descriptor // Proc of the 3rd International Conference on Crime Detection and Prevention. London, UK: IET, 2009: 1-6. [6] PFISTER T, LI X B, ZHAO G Y, et al. Recognising Spontaneous Facial Micro-expressions // Proc of the IEEE International Conference on Computer Vision. New York, USA: IEEE, 2011: 1449-1456. [7] WU Q, SHEN X B, FU X L. The Machine Knows What You Are Hiding: An Automatic Micro-expression Recognition System // Proc of the 4th International Conference on Affective Computing and Inte-lligent Interaction. Berlin, Germany: Springer, 2011, II: 152-162. [8] WANG S J, CHEN H L, YAN W J, et al. Face Recognition and Micro-expression Recognition Based on Discriminant Tensor Subspace Analysis Plus Extreme Learning Machine. Neural Processing Letters, 2014, 39(1): 25-43. [9] 张泽旭,李金宗,李冬冬.基于最小RMSE测度的多尺度微分光流算法.模式识别与人工智能, 2004, 17(3): 299-305. (ZHANG Z X, LI J Z, LI D D. A Multi-scale Differential Optical Flow Algorithm Based on Measure of the Least RMSE. Pattern Re-cognition and Artificial Intelligence, 2004, 17(3): 299-305.) [10] 张佳威,支瑞峰.光流算法比较分析研究.现代电子技术, 2013, 36(13): 39-42. (ZHANG J W, ZHI R F. Comparative Analysis of Optical Flow Algorithms. Modern Electronics Technique, 2013, 36(13): 39-42.) [11] SHREVE M, GODAVARTHY S, GOLDGOF D, et al. Macro- and Micro-expression Spotting in Long Videos Using Spatio-Temporal Strain // Proc of the IEEE International Conference on Automatic Face & Gesture Recognition and Workshops. New York, USA: IEEE, 2011: 51-56. [12] 张轩阁,田彦涛,郭艳君,等.基于光流与LBP-TOP特征结合的微表情识别.吉林大学学报(信息科学版), 2015, 33(5): 516-523. (ZHANG X G, TIAN Y T, GUO Y J, et al. Micro-expression Recognition Based on Feature Combination of Optical Flow and LBP-TOP. Journal of Jilin University(Information Science Edition), 2015, 33(5): 516-523.) [13] BROX T, BRUHN A, PAPENBERG N, et al. High Accuracy Optical Flow Estimation Based on a Theory for Warping // Proc of the 8th European Conference on Computer Vision. Berlin, Germany: Springer, 2004: 25-36. [14] LIU C. Beyond Pixels: Exploring New Representations and Applications for Motion Analysis. Ph.D Dissertation. Cambridge, USA: Massachusetts Institute of Technology, 2009. [15] 高 健,黄心汉,彭 刚,等.基于Harris角点和高斯差分的特征点提取算法.模式识别与人工智能, 2008, 21(2): 171-176. (GAO J, HUANG X H, PENG G, et al. A Feature Detection Method Based on Harris Corner and Difference of Gaussian. Pattern Recognition and Artificial Intelligence, 2008, 21(2): 171-176.) [16] SUN C Y, SANG N, ZHANG T X, et al. Image Bilinear Interpolation Enlargement and Calculation Analysis. Computer Engineering, 2005, 31(9): 167-168. [17] YAN W J, LI X B, WANG S J, et al. CASME II: An Improved Spontaneous Micro-expression Database and the Baseline Evaluation. PLOS One, 2014, 9(1). DOI: 10.1371/journal.pone.0086041.