DocumentCode
3138772
Title
A Linear Discriminant Analysis for Low Resolution Face Recognition
Author
Yeom, Seokwon
Author_Institution
Dept. of Comput. & Commun. Eng., Daegu Univ., Gyeongsan
Volume
3
fYear
2008
fDate
13-15 Dec. 2008
Firstpage
230
Lastpage
233
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;
fLanguage
English
Publisher
ieee
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
Type
conf
DOI
10.1109/FGCNS.2008.59
Filename
4813586
Link To Document