融合深度与手工特征，实时判断微笑真伪
Coupling deep and handcrafted features to assess smile genuineness
- 用LSTM学动态特征，手工提取面部动作单元变化
- 在真实视频上达到更高准确率，支持实时分析
- 适合情感计算、人机交互等需快速识别情绪的场景
从视频序列中评估微笑真伪是识别面部表情并关联其背后情绪状态的重要课题。已有方法多基于手工特征或深度学习提取特征，各有优劣。本文提出将长短期记忆网络学习的特征与手工设计的面部动作单元动态特征相结合。实验表明，该方法优于基线模型，能实现实时视频中微笑真伪的准确评估。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。