Generalized N-Dimensional Principal Component Analysis (GND-PCA) Based Statistical Appearance Modeling of Facial Images with Multiple Modes
Qiao Ping Xu, Rui Xu, Yen‐Wei Chen, Takanori Igarashi, Keisuke Nakao, Akio Kashimoto · IPSJ Transactions on Computer Vision and Applications · 2009
This paper introduces a framework called generalized N-dimensional principal component analysis (GND-PCA) for statistical appearance modeling of facial images with multiple modes including different people, different viewpoint and different illumination. The facial images with multiple modes can be considered as high-dimensional data. GND-PCA can represent the high-order dimensional data more efficiently. We conduct extensive experiments on MaVIC Database (KAO-Ritsumeikan Multi-angle View, Illumination and Cosmetic Facial Database) to evaluate the effectiveness of the proposed algorithm and compared the conventional ND-PCA in terms of reconstruction error. The results indicated that the extraction of data features is computationally more efficient using GND-PCA than PCA and ND-PCA.