Parallel Image Matrix Compression for Face Recognition

The canonical face recognition algorithm Eigenface and Fisherface are both based on one dimensional vector representation. However, with the high feature dimensions and the small training data, face recognition often suffers from the curse of dimension and the small sample problem. Recent research [4] shows that face recognition based on direct 2D matrix representation, i.e. 2DPCA, obtains better performance than that based on traditional vector representation. However, there are three questions left unresolved in the 2DPCA algorithm: 1) what is the meaning of the eigenvalue and eigenvector of the covariance matrix in 2DPCA; 2) why 2DPCA can outperform Eigenface; and 3) how to reduce the dimension after 2DPCA directly. In this paper, we analyze 2DPCA in a different view and proof that 2DPCA is actually a “localized” PCA with each row vector of an image as object. With this explanation, we discover the intrinsic reason that 2DPCA can outperform Eigenface is because fewer feature di...

• Type: Conference
• Year: 2005
• Where: MMM
• Authors: Dong Xu, Shuicheng Yan, Lei Zhang, Mingjing Li, Wei-Ying Ma, Zhengkai Liu, HongJiang Zhang
• Keywords: Dimension, Face Recognition, MMM 2005, Multimedia, Vector Representation