Robust facial expression feature extraction method is the key part of the computer aided facial expression analysis system.
面部表情特征抽取的针对性和鲁棒性在计算机辅助面部表情自动分析系统中具有举足轻重的作用。
Facial feature points localization takes an important role in the face recognition, facial expression analysis, cartoon face synthesis, etc.
人脸特征点的定位在人脸识别、人脸表情分析以及卡通人脸生成等方面具有非常重要的作用。
Given FACS's three decades of acceptance and CERT's record of accuracy, automated facial-expression analysis might well meet those criteria.
由于30年来FACS已得到承认和CERT的精确性记录，面部表情分析的自动化非常符合这些标准。
Recently, many novel methods are applied in the facial expression recognition such as Artificial Neural Networks, Support Vector Machines, Wavelet Analysis, Hide Markov Model and Optical Flow, etc.
近来，很多新的算法被应用在表情识别当中来，如：人工神经网络、支持向量机、小波分析、隐马尔可夫链模型和光流等。
Recently, many novel methods are applied in the facial expression recognition such as Artificial Neural Networks, Support Vector Machines, Wavelet Analysis, Hide Markov Model and Optical Flow, etc.
近来，很多新的算法被应用在表情识别当中来，如：人工神经网络、支持向量机、小波分析、隐马尔可夫链模型和光流等。
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