A comparative study on movement feature in different directions for micro-expression recognition
A comparative study on movement feature in different directions for micro-expression recognition
复制标题
DOI：
10.1016/j.neucom.2021.03.063
复制
发表时间：
2021-02
期刊：
影响因子：
--
通讯作者：
Jinsheng Wei;G. Lu;Jingjie Yan
中科院分区：
文献类型：
--
作者：
Jinsheng Wei;G. Lu;Jingjie Yan
微表情可以反映人的真实的情感。识别微表情很困难，因为它们是小动作，持续时间很短。随着对微表情识别研究的不断深入，人们提出了许多有效的特征和方法。为了确定哪个方向的运动特征更容易区分微表情，本文选取了18个方向（包括水平、垂直和倾斜三种类型的运动），提出了一种新的低维特征--单向梯度直方图（HSDG）来研究这一课题。本文将每个方向上的HSDG与LBP-TOP串联，得到具有单向梯度的LBP（LBP-SDG），并分析哪个方向的运动特征对微表情识别更具区分力。与一些现有的工作一样，欧拉视频放大（EVM）被用作预处理步骤。在CASME II和SMIC-HS数据库上的实验总结了有效和最佳方向，并证明了最佳方向上的HSDG是有区别的，并且相应的LBP-SDG达到了最先进的性能。
Micro-expression can reflect people’s real emotions. Recognizing micro-expressions is difficult because they are small motions and have a short duration. As the research is deepening into micro-expression recognition, many effective features and methods have been proposed. To determine which direction of movement feature is easier for distinguishing micro-expressions, this paper selects 18 directions (including three types of horizontal, vertical and oblique movements) and proposes a new low-dimensional feature called the Histogram of Single Direction Gradient (HSDG) to study this topic. In this paper, HSDG in every direction is concatenated with LBP-TOP to obtain the LBP with Single Direction Gradient (LBP-SDG) and analyze which direction of movement feature is more discriminative for micro-expression recognition. As with some existing work, Euler Video Magnification (EVM) is employed as a preprocessing step. The experiments on the CASME II and SMIC-HS databases summarize the effective and optimal directions and demonstrate that HSDG in an optimal direction is discriminative, and the corresponding LBP-SDG achieves state-of-the-art performance.