基于ME-ResNet人脸微表情识别方法

Face Micro-expression Recognition Method Based on ME-ResNet

人脸微表情具有持续时间短、动作幅度小的特点。数据集样本量较少等因素也给微表情识别带来了巨大挑战。针对上述问题,提出一种基于ME-ResNet残差网络的微表情识别方法。首先,在预处理阶段,等间隔提取微表情视频片段起始帧至顶点帧之间的关键帧序列,利用改进Farneback光流法提取微表情关键帧序列的面部光流运动特征;接着,构建基于3D卷积的ResNet50网络,并将空间通道注意力CBAM机制加入网络Bottleneck模块,以增强网络对面部关键运动特征的聚焦学习能力,并构建ME-ResNet网络模型,将所提取的面部光流运动特征送入网络进行训练;最后,使用数据增强增加网络训练样本量,将ME-ResNet网络模型用于微表情识别任务,并在CASMEII,SMIC和SAMM数据集上进行实验验证,所提算法识别率达到了84.42%,72.56%,70.41%,与其他算法相比具有较高的识别能力。

Face micro-expressions have the characteristics of short duration and small amplitude of movement.Factors such as the small sample size of dataset also bring great challenges to micro-expression recognition.To solve these problems,this paper proposes a micro-expression recognition method based on ME-ResNet residual network.First,in the pre-processing stage,extract the key frame sequence between the start frame and the vertex frame in the micro-expression video clip at equal intervals and then,use the improved Farneback optical flow method to extract the motion features of the micro-expression key frame sequence.Se-cond,construct a ResNet50 network based on 3D convolution and add the spatial channel attention CBAM mechanism to the network Bottleneck module,so as to enhance the ability to focus on key facial motor features.Next,construct the ME-ResNet network model and sent the extracted facial optical flow motion features to the network for training.Finally,use the data enhancement to increase the sample size of network training and apply the ME-ResNet network model to micro-expression recognition tasks.Also,experimental results on CASME II,SMIC and SAMM datasets show that the recognition rate of the proposed algorithm reaches 84.42%,72.56% and 70.41% respectively.It has higher recognition ability compared with other algorithms.