A Linear Discriminant Analysis for Low Resolution Face Recognition
Seokwon Yeom · 2008
This invited paper discusses low resolution face recognition using photon-counting linear discriminant analysis (LDA). The photon-counting LDA asymptotically realizes the Fisher criterion without dimensionality reduction. Linear boundaries are determined in high dimensional space to classify unknown objects. It will be shown that the proposed method provides better results than eigen face and Fisher face in terms of accuracy and false alarm rates.