论文标题
移动设备的面部表情，价，唤醒和动作单元的框架级别预测
Frame-level Prediction of Facial Expressions, Valence, Arousal and Action Units for Mobile Devices
论文作者
论文摘要
在本文中，我们考虑了基于实时视频的面部情感分析的问题，即面部表达识别，价值和唤醒的预测以及动作单位点的检测。我们通过提取对altimpnet预先训练的单个有效网络模型提取面部特征来提出新型帧级情绪识别算法。结果，我们的方法甚至可以用于移动设备上的视频分析。来自第三个情感行为分析（ABAW）竞争的大规模AFF-WILD2数据库的实验结果表明，与VGGFACE基线相比，我们的简单模型要好得多。特别是，我们的方法的特征在于0.15-0.2较高的性能度量，用于在单项任务表达式分类，价值估计和表达式分类中进行验证集。由于简单，我们的方法可能被视为所有四个子挑战的新基线。
In this paper, we consider the problem of real-time video-based facial emotion analytics, namely, facial expression recognition, prediction of valence and arousal and detection of action unit points. We propose the novel frame-level emotion recognition algorithm by extracting facial features with the single EfficientNet model pre-trained on AffectNet. As a result, our approach may be implemented even for video analytics on mobile devices. Experimental results for the large scale Aff-Wild2 database from the third Affective Behavior Analysis in-the-wild (ABAW) Competition demonstrate that our simple model is significantly better when compared to the VggFace baseline. In particular, our method is characterized by 0.15-0.2 higher performance measures for validation sets in uni-task Expression Classification, Valence-Arousal Estimation and Expression Classification. Due to simplicity, our approach may be considered as a new baseline for all four sub-challenges.
