基于有监督核局部线性嵌入的面部表情识别

黄鸿; 李见为; 冯海亮

光学精密工程, 2008, 16 (8): 1471, 网络出版: 2010-02-28

基于有监督核局部线性嵌入的面部表情识别

Facial expression recognition based on supervised kernel local linear embedding

论文大纲

黄鸿 ^*李见为冯海亮

作者单位

重庆大学光电技术及系统教育部重点实验室,重庆 400030

流形学习核技巧局部线性嵌入有监督学习面部表情识别 manifold learning kernel trick Local Linear Embedding(LLE) supervised learning facial expression recognition

摘要

提出了一种新的有监督核局部线性嵌入算法(SKLLE),并将算法应用于面部表情识别中。该算法通过非线性核映射将人脸图像样本投影到高维核空间,然后将人脸图像局部流形的结构信息和样本的类别信息有效地结合进行维数约简,提取低维鉴别流形特征用于表情分类。SKLLE算法不仅能发现嵌入了高维人脸图像空间的低维表情子流形,增强了局部类间的联系,而且对新样本有较好的泛化性。基于JAFFE面部表情库的实验结果表明,该方法能很好地实现维数约简,达到最高识别率(100%)所需的鉴别维数仅为二维,有效地提高了面部表情识别的性能。

Abstract

A novel supervised kernel local linear embedding(SKLLE) method is introduced to facial expression recognition,which maps face images to a high dimensional kernel space through nonlinear kernel mapping,then fuses prior class-label information and nonlinear facial expression submanifold of real face images to extract discriminative features for expression classification.SKLLE can not only gain a perfect approximation of facial expression manifold,and enhance local within-class relations,but also can do well on the new samples.The experimental results on JAFFE database show that the proposed method can achieve the highest recognition rate(100%) using only 2D embedding feature vectors,which improves face expression classification performance effectively.

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黄鸿, 李见为, 冯海亮. 基于有监督核局部线性嵌入的面部表情识别[J]. 光学精密工程, 2008, 16(8): 1471. HUANG Hong, LI Jian-wei, FENG Hai-liang. Facial expression recognition based on supervised kernel local linear embedding[J]. Optics and Precision Engineering, 2008, 16(8): 1471.

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