Generalised nearest feature line for subspace learning
Authors:
- 27 September 2007
- journal article
- Published by Institution of Engineering and Technology (IET) in Electronics Letters
- Vol. 43 (20) , 1079-1080
Abstract
Nearest feature line (NFL) is a powerful tool in pattern recognition, which generalises the representational capacity of available prototypes by using linear interpolation and extrapolation between the feature points. NFL is generalised to subspace learning so that the obtained subspace has desirable discriminating ability. Experiments on face recognition demonstrate its effectiveness.Keywords
This publication has 8 references indexed in Scilit:
- Nearest neighbour line nonparametric discriminant analysis for feature extractionElectronics Letters, 2006
- PCA versus LDAIEEE Transactions on Pattern Analysis and Machine Intelligence, 2001
- Performance evaluation of the nearest feature line method in image classification and retrievalIEEE Transactions on Pattern Analysis and Machine Intelligence, 2000
- The FERET evaluation methodology for face-recognition algorithmsIEEE Transactions on Pattern Analysis and Machine Intelligence, 2000
- Face recognition using the nearest feature line methodIEEE Transactions on Neural Networks, 1999
- Ar Rasfa, A Levantine Mousterian Site from Northwest Jordan : a Preliminary ReportPaléorient, 1998
- Eigenfaces vs. Fisherfaces: recognition using class specific linear projectionIEEE Transactions on Pattern Analysis and Machine Intelligence, 1997
- Eigenfaces for RecognitionJournal of Cognitive Neuroscience, 1991