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Statistical Modeling for Multiple Modes Facial Images using GND-PCA
Qiao Xu * #
School of Control Science and Engineering,Shandong University,Jinan 250061,China
*Correspondence author
#Submitted by
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Funding: Doctoral Fund of Ministry of Education of China（No.No.20130131120035）
Opened online:11 May 2017
Accepted by: none
Citation: Qiao Xu.Statistical Modeling for Multiple Modes Facial Images using GND-PCA[OL]. [11 May 2017] http://en.paper.edu.cn/en_releasepaper/content/4732187
 
 
In this paper, we proposed a new approach called generalized N-dimensional principal component analysis (GND-PCA) for statistical appearance modeling of facial images with multiple modes including different people, different pose and different illumination. The facial images with multiple modes can be considered as high-dimensional data. GND-PCA can be used to treat the high-order dimensional data as a series of high-order tensors and calculate the bases on each mode-subspace in order to approximate the tensor accurately. GND-PCA can represent the high-order dimensional data of image ensembles more efficiently compared to the recently proposed ND-PCA method. MaVIC Database (KAO-Ritsumeikan Multi-angle View, Illumination and Cosmetic Facial Database) is used in our experiments and the results are compared with those obtained by conventional PCA and ND-PCA.
Keywords:statistical modeling; principal component analysis; multiple mode
 
 
 

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