Appearance Global and Local Structure Fusion for Face Image Recognition
Abstract: Principal component
analysis (PCA) and linear descriminant analysis (LDA) are an extraction method
based on appearance with the global structure features. The global structure
features have a weakness; that is the local structure features can not be
characterized. Whereas locality preserving projection (LPP) and orthogonal
laplacianfaces (OLF) methods are an appearance extraction with the local
structure features, but the global structure features are ignored. For both the
global and the local structure features are very important. Feature extraction
by using the global or the local structures is not enough. In this research, it
is proposed to fuse the global and the local structure features based on
appearance. The extraction results of PCA and LDA methods are fused to the
extraction results of LPP. Modelling results were tested on the Olivetty
Research Laboratory database face images. The experimental results show that
our proposed method has achieved higher recognation rate than PCA, LDA, LPP and
OLF Methods.
Author: Arif Muntasa, Indah
Agustien Sirajudin, Mauridhi Hery Purnomo
Journal Code: jptkomputergg110019