Kernel Fisher NPE for Face Recognition

Authors:
Guoqiang Wang;Zongying Ou;Fan Ou;Dianting Liu;Feng Han
Affiliations:
Key Laboratory for Precision and Non-traditional Machining Technology of Ministry of, Education, Dalian University of Technology, Dalian 116024, P. R. China;Key Laboratory for Precision and Non-traditional Machining Technology of Ministry of, Education, Dalian University of Technology, Dalian 116024, P. R. China;Key Laboratory for Precision and Non-traditional Machining Technology of Ministry of, Education, Dalian University of Technology, Dalian 116024, P. R. China;Key Laboratory for Precision and Non-traditional Machining Technology of Ministry of, Education, Dalian University of Technology, Dalian 116024, P. R. China;Changchun Railway Vehicles Limited Company, Changchun 130062, China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Year:
2007

Citing 4
Cited 0

The nature of statistical learning theory

The nature of statistical learning theory
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Neighborhood Preserving Embedding

ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Eigenfaces for recognition

Journal of Cognitive Neuroscience

Quantified Score

Hi-index	0.00

Visualization

Abstract

Neighborhood Preserving Embedding (NPE) is a subspace learning algorithm. Since NPE is a linear approximation to Locally Linear Embedding (LLE) algorithm, it has good neighborhood-preserving properties. Although NPE has been applied in many fields, it has limitations to solve recognition task. In this paper, a novel subspace method, named Kernel Fisher Neighborhood Preserving Embedding (KFNPE), is proposed. In this method, discriminant information as well as the intrinsic geometry relations of the local neighborhoods are preserved according to prior class-label information. Moreover, complex nonlinear variations of real face images are represented by nonlinear kernel mapping. Experimental results on ORL face database demonstrate the effectiveness of the proposed method.