计算机工程 ›› 2011, Vol. 37 ›› Issue (15): 171-173,176.doi: 10.3969/j.issn.1000-3428.2011.15.054

• 人工智能及识别技术 • 上一篇    下一篇

基于混合特征和分层最近邻法的人脸表情识别

王晓霞,李振龙,辛 乐   

  1. (北京工业大学电子信息与控制工程学院,北京 100124)
  • 收稿日期:2011-01-11 出版日期:2011-08-05 发布日期:2011-08-05
  • 作者简介:王晓霞(1986-),女,硕士研究生,主研方向:表情识别,图像处理;李振龙,副教授、博士;辛 乐,讲师、博士

Human Facial Expression Recognition Based on Mixed Features and Hierarchical Nearest Neighbor Method

WANG Xiao-xia, LI Zhen-long, XIN Le   

  1. (School of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China)
  • Received:2011-01-11 Online:2011-08-05 Published:2011-08-05

摘要: 采用主动形状模型提取人脸嘴巴几何特征,利用Gabor小波提取眼睛和眉毛频域特征。根据人脸表情特征基于分类树思想将表情进行三层分类。第1层以嘴宽高比、嘴高、嘴宽高差作为最近邻的输入进行训练实现粗分类;第2层以嘴宽、嘴宽高差作为最近邻的输入进行训练实现分类;第3层以眼睛和眉毛区域15个关键点的Gabor小波特征作为最近邻的输入进行训练实现细致的分类。整个识别过程由粗到细,融合了几何特征和频域特征。实验结果表明该方法是有效的。

关键词: 表情识别, 混合特征, 主动形状模型, Gabor小波, 最近邻法

Abstract: Geometry feature of the mouth is extracted by using Active Shape Model(ASM), and frequency domain feature of the eye and eyebrow is extracted by using Gabor wavelet. According to characteristics of human facial expression, three-layer classification is proposed based on classification tree. The width, the ratio and the difference of width and height of mouth are taken as input of Nearest Neighbor(NN) to train and classify at the first layer. The width and the difference of width and height of mouth are taken as input of NN to train and classify at the second layer. The Gabor wavelet feature of 15 key dots of the eye and eyebrow are taken as input of NN to train and classify at the third layer. The whole identification process is from coarse to fine and combines geometric features and frequency domain features. Experimental results show that the approach is effective.

Key words: expression recognition, mixed features, Active Shape Model(ASM), Gabor wavelet, Nearest Neighbor(NN) method

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