Fusion of Geometrical and Texture Information for Facial Expression Recognition
Fusion of Geometrical and Texture Information for Facial Expression Recognition
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DOI：
10.1109/icip.2006.313054
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发表时间：
2006-10
期刊：
影响因子：
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通讯作者：
I. Kotsia;N. Nikolaidis;I. Pitas
中科院分区：
文献类型：
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作者：
I. Kotsia;N. Nikolaidis;I. Pitas
提出了一种基于几何和纹理信息的人脸表情识别方法。该算法对视频序列的最后一帧图像进行判别式非负矩阵分解（DNMF），提取人脸表情的最大强度对应的纹理信息。一个支持向量机（SVM）系统被用于分类的几何信息来自跟踪的视频序列上的交错网格。几何信息由中性（第一）和完全表达的面部表情（最后）视频帧之间的节点坐标的差异组成。使用支持向量机进行纹理和几何信息的融合。在识别六种基本面部表情时，准确率达到98.7%。
A novel method based on geometrical and texture information is proposed for facial expression recognition from video sequences. The discriminant non-negative matrix factorization (DNMF) algorithm is applied at the image of the last frame of the video sequence, corresponding to the greatest intensity of the facial expression, thus extracting the texture information. A support vector machines (SVMs) system is used for the classification of the geometrical information derived from tracking the Candide grid over the video sequence. The geometrical information consists of the differences of the node coordinates between the neutral (first) and the fully expressed facial expression (last) video frame. The fusion of texture and geometrical information obtained is performed using SVMs. The accuracy achieved is 98,7% when recognizing the six basic facial expressions.