Fully Automated Recognition of Spontaneous Facial Expressions in Videos Using Random Forest Classifiers
Fully Automated Recognition of Spontaneous Facial Expressions in Videos Using Random Forest Classifiers
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DOI：
10.1109/taffc.2014.2317711
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发表时间：
2014-04-01
影响因子：
11.2
通讯作者：
Levine, Martin D.
中科院分区：
文献类型：
--
作者：
Abd El Meguid, Mostafa K.;Levine, Martin D.
本文讨论了全自动综合面部表情检测和分类框架的设计和实现。它使用专有的人脸检测器 (PittPatt) 和新颖的分类器，该分类器由一组随机森林与支持向量机标记器配对组成。该系统在成像条件下以实时速率执行，无需中间人为干预。宾厄姆顿大学的表演静态图像 3D 面部表情数据库用于训练目的，而一些自发表情标记的视频数据库用于测试。定性和直观的面部表情识别的定量证据构成了该领域的主要理论贡献。
This paper discusses the design and implementation of a fully automated comprehensive facial expression detection and classification framework. It uses a proprietary face detector (PittPatt) and a novel classifier consisting of a set of Random Forests paired with support vector machine labellers. The system performs at real-time rates under imaging conditions, with no intermediate human intervention. The acted still-image Binghamton University 3D Facial Expression database was used for training purposes, while a number of spontaneous expression-labelled video databases were used for testing. Quantitative evidence for qualitative and intuitive facial expression recognition constitutes the main theoretical contribution to the field.