Assessing smile genuineness from video sequences is a vital topic concerned with recognizing facial expression and linking them with the underlying emotional states. There have been a number of techniques proposed underpinned with handcrafted features, as well as those that rely on deep learning to elaborate the useful features. As both of these approaches have certain benefits and limitations, in this work we propose to combine the features learned by a long short-term memory network with the features handcrafted to capture the dynamics of facial action units. The results of our experiments indicate that the proposed solution is more effective than the baseline techniques and it allows for assessing the smile genuineness from video sequences in real-time.

翻译：从视频序列中评估微笑真实性是一个重要课题，涉及面部表情识别及其与潜在情感状态的关联。现有技术既有基于手工特征的方法，也有依赖深度学习提取有效特征的方法。鉴于这两种方法各具优势与局限，本研究提出将长短期记忆网络学习到的特征与手工设计的特征相结合，以捕捉面部动作单元的动态变化。实验结果表明，所提出的解决方案比基线技术更为有效，并能实现视频序列中微笑真实性的实时评估。