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基于典型相关分析特征融合的人脸表情识别方法

张建明 杨丽瑞 王良民   

  1. 江苏大学计算机科学与通信工程学院 江苏大学计算机科学与通信工程学院 江苏大学计算机科学与通信工程学院
  • 收稿日期:2007-10-10 修回日期:1900-01-01 发布日期:2008-03-01 出版日期:2008-03-01
  • 通讯作者: 张建明

Facial expression recognition based on hybrid features fused by CCA

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  • Received:2007-10-10 Revised:1900-01-01 Online:2008-03-01 Published:2008-03-01
  • Contact: Jian-ming Zhang

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摘要： 针对目前大部分人脸表情识别算法中仅提取图像的某一类特征，导致特征参数不能全面反映脸部情感信息的问题，提出了一种基于特征融合和离散隐马尔可夫模型（HMM）识别的人脸表情识别方法。对同一个图像序列分别使用离散小波变换(DWT)和标准正交非负矩阵分解(ONMF)提取纹理信息，使用改进的主动表观模型(AAM)提取几何形变信息，再使用高维小样本下典型相关分析(CCA)对提取的两种特征进行特征融合，最后使用离散HMM来进行表情分类识别。实验结果表明，经过特征融合后，在较少特征向量维数下该方法能够达到较高的识别率和较快的识别速度。

关键词: 表情识别, 离散小波交换, 标准正交非负矩阵分解, 主观表现模型, 典型相关分析, 隐马尔可夫模型

Abstract: To overcome the disadvantage of traditional facial expression recognition methods that generally use single feature extraction methods, which can not extract effective features currently, a new method based on fused hybrid features and discrete Hidden Markov Model （HMM） for facial expression recognition from image sequences was presented. For the same image sequence, we used Discrete Wavelet Transformation （DWT） and Orthonormal Non-Negative Matrix Factorization （ONMF） to extract texture information toward pure faces and used Active Appearance Model （AAM） to extract geometrical shaped information. In order to reduce the redundancy, Canonical Correlation Analysis （CCA） was used to fuse the two features for observation vectors of discrete HMM. Experiments show that the method can get high recognition rate after fused with fewer feature vectors.

Key words: facial expression recognition, Discrete Wavelet Transformation （DWT）, Orthonormal Non-Negative Matrix Factorization （ONMF）, Active Appearance Model （AAM）, Canonical Correlation Analysis （CCA）, Hidden Markov Model （HMM）

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