Title :
A Linear Discriminant Analysis for Low Resolution Face Recognition
Author_Institution :
Dept. of Comput. & Commun. Eng., Daegu Univ., Gyeongsan
Abstract :
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.
Keywords :
eigenvalues and eigenfunctions; face recognition; image resolution; Fisher criterion; eigen face; face recognition; false alarm rates; photon-counting linear discriminant analysis; Computer networks; Conferences; Covariance matrix; Face recognition; Image resolution; Linear discriminant analysis; Optical computing; Pixel; Surveillance; Training data; Face recognition; Fisher LDA; Low resolution; Object classification; Photon counting linear discriminant analysis;
Conference_Titel :
Future Generation Communication and Networking Symposia, 2008. FGCNS '08. Second International Conference on
Conference_Location :
Sanya
Print_ISBN :
978-1-4244-3430-5
Electronic_ISBN :
978-0-7695-3546-3
DOI :
10.1109/FGCNS.2008.59
