In this study, we present a fusion model for emotion recognition based on visual data. The proposed model uses video information as its input and generates emotion labels for each video sample. Based on the video data, we first choose the most significant face regions with the use of a face detection and selection step. Subsequently, we employ three CNN-based architectures to extract the high-level features of the face image sequence. Furthermore, we adjusted one additional module for each CNN-based architecture to capture the sequential information of the entire video dataset. The combination of the three CNN-based models in a late-fusion-based approach yields a competitive result when compared to the baseline approach while using two public datasets: AFEW 2016 and SAVEE.

在这项研究中，我们提出了一种基于视觉数据的情感识别融合模型。所提出的模型使用视频信息作为其输入，并为每个视频样本生成情感标签。根据视频数据，我们首先使用面部检测和选择步骤选择最重要的面部区域。随后，我们采用了三种基于CNN的架构来提取人脸图像序列的高级特征。此外，我们针对每个基于CNN的体系结构调整了一个附加模块，以捕获整个视频数据集的顺序信息。与基线方法相比，在使用两个公共数据集（AFEW 2016和SAVEE）的情况下，将三种基于CNN的模型与基于后期融合的方法相结合可产生竞争性结果。