摘要
由于对局部纹理特征具有很强的描述能力,LBP(Local Binary Patterns)已经被广泛应用于模式识别、计算机视觉等相关领域,但传统的LBP在表情识别中的正确率并不高,提出了一种结合小波分解的改进LBP特征提取方法,首先使用Adaboost人脸检测算法和2D模型提取人脸图像并归一化,并使用小波分解的方法增强LBP特征,然后通过AVR(Augmented Variance Ratio)特征选取方法降维,最后使用SVM进行分类。JAFFE库上的实验证明了该方法的有效性。
As the excellent capability of description of local texture,Local Binary Patterns（LBP） has been applied in many areas such as pattern recognition and computer vision.But the traditional LBP can not get good results in facial expression recognition. In this paper an improved LBP feature extraction method combined with wavelet decomposition is developed.First the face image is extracted and normalized through Adaboost face detection algorithm and 2D model,and then wavelet decomposition is applied to enhance LBP features,and the most effective features are selected through Augmented Variance Ratio（AVR） methods to reduce the dimension,and finally the Support Vector Machine（SVM） classifier is used to classify face images with enhanced LBP features.The experiment operated on JEFFE face image database proofs the effectiveness of the proposed method.
出处
《计算机工程与应用》
CSCD
北大核心
2009年第19期184-188,共5页
Computer Engineering and Applications
基金
国家高技术研究发展计划(863)(No.2007AA01Z157)
上海市科学技术委员会科研计划项目(No.06DZ15008)~~
关键词
人脸表情识别
LBP特征
AVR特征选取
SVM分类器
facial expression recognition
Local Binary Patterns （LBP） features
Augmented Variance Ratio （AVR） feature selection
Support Vector Machine（SVM） classifier
