基于全局光流特征的微表情识别

Micro-expression Recognition Based on Global Optical Flow Feature

以改善微表情识别效果为目标,研究基于梯度的全局光流特征提取算法.针对精细图像间大位移问题,引入多分辨率策略对图像分层,通过迭代重加权最小二乘法逐层优化目标函数,求解最优光流,保证运动跟踪的准确性.为了体现人脸关键部位的动作差异,提出分区的特征统计方法,将光流图像划分为若干矩形区域,在局部区域内归纳各点光流运动情况,增强特征的有效性.实验表明,文中方法提升整体识别率和各类情感区分的准确度.

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.