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Features classification using geometrical deformation feature vector of support vector machine and active appearance algorithm for automatic facial expression recognition

机译：利用支持向量机的几何变形特征向量和主动出现算法进行面部表情自动识别的特征分类

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摘要

This paper proposes a method for facial expression recognition in image sequences. Face is detected from the scene and then facial features are detected using image normalization, and thresholding techniques. Using an optimization algorithm the Candide wire frame model is adapted properly on the first frame of face image sequence. In the subsequent frames of image sequence facial features are tracked using active appearance algorithm. Once the model fits on the first frame, animation parameters of model are set to zero, to obtain the shape of model for the neutral facial expression of the same face. The last frame of the image sequence corresponds to greatest facial expression intensity. The geometrical displacement of the Candide wire frame nodes, between the neutral expression frame and the last frame, is used as an input to the multiclass support vector machine, which classifies facial expression into one of the class such as happy, surprise, sadness, anger, disgust, fear and neutral. This method is applicable for frontal as well as tilted faces with angle ±30 °, ±45 °, ±60 ° with respect to y axis.
机译：本文提出了一种在图像序列中进行面部表情识别的方法。从场景中检测到面部，然后使用图像归一化和阈值技术检测面部特征。使用优化算法，可以在人脸图像序列的第一帧上适当调整Candide线框模型。在图像序列的后续帧中，使用主动外观算法跟踪面部特征。一旦模型适合第一帧，就将模型的动画参数设置为零，以获得用于同一张脸的中性面部表情的模型形状。图像序列的最后一帧对应最大的面部表情强度。中性表情框和最后一帧之间的Candide线框节点的几何位移用作多类支持向量机的输入，该支持向量机将面部表情分为快乐，惊奇，悲伤，愤怒等类别之一，厌恶，恐惧和中立。此方法适用于相对于y轴的角度为±30°，±45°，±60°的正面和倾斜面。

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