Weighted-PCANet for Face Recognition

摘要

Weighted-PCANet, a novel feature learning method is proposed to face recognition by combining Linear Regression Classification model (LRC) and PCANet construction. The sample specific hat matrix is used to handle different images in feature extraction stage. After appropriate adaption, the performance of this new model outperform than various mainstream methods including PCANet for face recognition on Extended YaleB dataset. Particularly, various experiments testify the robustness of weighted-PC ANet while dealing with less training samples or corrupted data.