Kotsia Irene, Pitas Ioannis
Department of Informatics, Aristotle University of Thessaloniki, Greece.
IEEE Trans Image Process. 2007 Jan;16(1):172-87. doi: 10.1109/tip.2006.884954.
In this paper, two novel methods for facial expression recognition in facial image sequences are presented. The user has to manually place some of Candide grid nodes to face landmarks depicted at the first frame of the image sequence under examination. The grid-tracking and deformation system used, based on deformable models, tracks the grid in consecutive video frames over time, as the facial expression evolves, until the frame that corresponds to the greatest facial expression intensity. The geometrical displacement of certain selected Candide nodes, defined as the difference of the node coordinates between the first and the greatest facial expression intensity frame, is used as an input to a novel multiclass Support Vector Machine (SVM) system of classifiers that are used to recognize either the six basic facial expressions or a set of chosen Facial Action Units (FAUs). The results on the Cohn-Kanade database show a recognition accuracy of 99.7% for facial expression recognition using the proposed multiclass SVMs and 95.1% for facial expression recognition based on FAU detection.
本文提出了两种用于面部图像序列中面部表情识别的新方法。用户必须手动将一些坎迪德网格节点放置到正在检查的图像序列第一帧中描绘的面部地标上。所使用的基于可变形模型的网格跟踪和变形系统，随着面部表情的演变，在连续的视频帧中跟踪网格，直到对应最大面部表情强度的帧。某些选定的坎迪德节点的几何位移，定义为第一帧和最大面部表情强度帧之间节点坐标的差值，被用作一种新型多类支持向量机（SVM）分类器系统的输入，该系统用于识别六种基本面部表情或一组选定的面部动作单元（FAU）。在科恩 - 卡纳德数据库上的结果表明，使用所提出的多类支持向量机进行面部表情识别的准确率为99.7%，基于FAU检测的面部表情识别准确率为95.1%。
