Facial Expression Recognition using AAM and Local Facial Features

Fangqi Tang, Benzai Deng
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引用次数: 35

Abstract

A new technique for facial expression recognition is proposed, which uses active appearance model (AAM) to extract facial feature points and combines useful local shape features to form a classifier. To enhance performance of AAM, we use Adaboost to locate eye position to initialize AAM. After extraction of facial feature points, we analyze local facial changes and use some simple features to form an effective classifier. At last, we demonstrate our approach by experiments.
基于AAM和局部面部特征的面部表情识别
提出了一种新的面部表情识别技术,利用主动外观模型(AAM)提取面部特征点,并结合有用的局部形状特征组成分类器。为了提高AAM的性能,我们使用Adaboost定位眼位来初始化AAM。在提取人脸特征点后,对局部人脸变化进行分析,并利用一些简单的特征组成有效的分类器。最后,通过实验对该方法进行了验证。
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