Title :
ECA and 2DECA: Entropy contribution based methods for face recognition inspired by KECA
Author :
Liu, Xing ; Wu, Xiao-jun
Author_Institution :
Sch. of IoT Eng., Jiangnan Univ., Wuxi, China
Abstract :
In this paper, two new methods: ECA and 2DECA are proposed for face recognition, which are inspired by KECA. In ECA (2DECA), features are selected in PCA (2DPCA) subspace based on the Renyi entropy contribution instead of cumulative variance contribution. Then the proposed methods are tested on the OLR, YALE and XM2VTS databases respectively. We also compare the performance of the related methods experimentally.
Keywords :
entropy; face recognition; feature extraction; principal component analysis; visual databases; 2DECA method; 2DPCA subspace; ECA method; KECA; OLR database; Renyi entropy contribution; XM2VTS database; YALE database; entropy contribution based method; face recognition; feature selection; Covariance matrix; Databases; Entropy; Feature extraction; Principal component analysis; Training; Vectors; 2DECA; ECA; Entropy contribution; Face recognition; Subspace;
Conference_Titel :
Soft Computing and Pattern Recognition (SoCPaR), 2011 International Conference of
Conference_Location :
Dalian
Print_ISBN :
978-1-4577-1195-4
DOI :
10.1109/SoCPaR.2011.6089154
