TY - JOUR
T1 - Two-Dimensional Whitening Reconstruction for Enhancing Robustness of Principal Component Analysis
AU - Shi, Xiaoshuang
AU - Guo, Zhenhua
AU - Nie, Feiping
AU - Yang, Lin
AU - You, Jane
AU - Tao, Dacheng
N1 - Publisher Copyright:
© 1979-2012 IEEE.
PY - 2016/10/1
Y1 - 2016/10/1
N2 - Principal component analysis (PCA) is widely applied in various areas, one of the typical applications is in face. Many versions of PCA have been developed for face recognition. However, most of these approaches are sensitive to grossly corrupted entries in a 2D matrix representing a face image. In this paper, we try to reduce the influence of grosses like variations in lighting, facial expressions and occlusions to improve the robustness of PCA. In order to achieve this goal, we present a simple but effective unsupervised preprocessing method, two-dimensional whitening reconstruction (TWR), which includes two stages: 1) A whitening process on a 2D face image matrix rather than a concatenated 1D vector; 2) 2D face image matrix reconstruction. TWR reduces the pixel redundancy of the internal image, meanwhile maintains important intrinsic features. In this way, negative effects introduced by gross-like variations are greatly reduced. Furthermore, the face image with TWR preprocessing could be approximate to a Gaussian signal, on which PCA is more effective. Experiments on benchmark face databases demonstrate that the proposed method could significantly improve the robustness of PCA methods on classification and clustering, especially for the faces with severe illumination changes.
AB - Principal component analysis (PCA) is widely applied in various areas, one of the typical applications is in face. Many versions of PCA have been developed for face recognition. However, most of these approaches are sensitive to grossly corrupted entries in a 2D matrix representing a face image. In this paper, we try to reduce the influence of grosses like variations in lighting, facial expressions and occlusions to improve the robustness of PCA. In order to achieve this goal, we present a simple but effective unsupervised preprocessing method, two-dimensional whitening reconstruction (TWR), which includes two stages: 1) A whitening process on a 2D face image matrix rather than a concatenated 1D vector; 2) 2D face image matrix reconstruction. TWR reduces the pixel redundancy of the internal image, meanwhile maintains important intrinsic features. In this way, negative effects introduced by gross-like variations are greatly reduced. Furthermore, the face image with TWR preprocessing could be approximate to a Gaussian signal, on which PCA is more effective. Experiments on benchmark face databases demonstrate that the proposed method could significantly improve the robustness of PCA methods on classification and clustering, especially for the faces with severe illumination changes.
KW - PCA
KW - preprocessing
KW - robustness
KW - Two-dimensional whitening reconstruction
UR - http://www.scopus.com/inward/record.url?scp=84986321455&partnerID=8YFLogxK
U2 - 10.1109/TPAMI.2015.2501810
DO - 10.1109/TPAMI.2015.2501810
M3 - 文章
AN - SCOPUS:84986321455
SN - 0162-8828
VL - 38
SP - 2130
EP - 2136
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
JF - IEEE Transactions on Pattern Analysis and Machine Intelligence
IS - 10
M1 - 7331304
ER -