Recognition Of Smiling Faces Using Neural Networks And Spca
Recognition Of Smiling Faces Using Neural Networks And Spca
复制标题
使用神经网络和 SPCA 识别笑脸
DOI：
10.1142/s1469026804001215
复制
发表时间：
2004
期刊：
影响因子：
--
通讯作者：
M. Fukumi
中科院分区：
文献类型：
--
作者：
M. Nakano;F. Yasukata;M. Fukumi
“人机界面”的研究在许多工程领域都得到了加强，并有望将其应用于人脸表情识别。基于主成分分析(PCA)的特征脸方法是这一领域的研究热点。然而，如果考虑到将其用于时变处理时的计算成本，则计算大矩阵的特征向量并不容易。为了实现快速的主成分分析，采用简单主成分分析(SPCA)对构成人脸的部分进行了降维处理。使用SPCA的特征向量以及每个画面图案的灰度图像向量来计算cosθ的值。通过使用神经网络(NNS)，澄清了真假(塑料)微笑的cosθ值的差异，并对真实微笑进行了区分。最后，为了验证本文提出的人脸真假分类方法的有效性，对真实的人脸图像进行了计算机仿真。此外，还进行了使用自组织映射(SOM)的实验作为比较。
Research on "man-machine interface" has increased in many fields of engineering and its application to facial expressions recognition is expected. The eigenface method by using the principal component analysis (PCA) is popular in this research field. However, it is not easy to compute eigenvectors with a large matrix if the cost of calculation when applying it for time-varying processing is taken into consideration. In this paper, in order to achieve high-speed PCA, the simple principal component analysis (SPCA) is applied to compress the dimensionality of portions that constitute a face. A value of cos θ is calculated using an eigenvector by SPCA as well as a gray-scale image vector of each picture pattern. By using neural networks (NNs), the difference in the value of cos θ between the true and the false (plastic) smiles is clarified and the true smile is discriminated. Finally, in order to show the effectiveness of the proposed face classification method for true or false smiles, computer simulations are done with real images. Furthermore, an experiment using the self-organisation map (SOM) is also conducted as a comparison.
基于特征脸的人脸建模与识别
DOI：
--
发表时间：
2003
期刊：
IPSJ SIG Technical Reports Vol. CVIM-139
影响因子：
--
作者：
T.;Shakunaga;F.;Sakaue;Y.;Matsubara
通讯作者：
Matsubara