Face recognition using KFDA-LLE

Authors:
Guoqiang Wang;Guoqiang Ding
Affiliations:
Department of Computer and Information Engineering, Luoyang Institute of Science and Technology, Luoyang, Henan, P.R. China;Department of Computer and Information Engineering, Luoyang Institute of Science and Technology, Luoyang, Henan, P.R. China
Venue:
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing
Year:
2011

Citing 4
Cited 0

Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods

FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Face Recognition Using Kernel Based Fisher Discriminant Analysis

FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Eigenfaces for recognition

Journal of Cognitive Neuroscience

Quantified Score

Hi-index	0.00

Visualization

Abstract

Locally Linear Embedding (LLE) is a recently proposed algorithm for non-linear dimensionality reduction and manifold learning. However, since LLE aims to discover the intrinsical low dimensional variables from high dimensional nonlinear data, it may not be optimal for classification problem. In this paper, an improved version of LLE, namely KFDA-LLE, is proposed using kernel Fisher discriminant analysis (KFDA) method for face recognition task. First, the input training samples are projected into the low-dimensional space by LLE. Then KFDA is introduced for finding the optimal projection direction. Experimental results on face database demonstrate that the proposed method excels the other methods.