Gabor-based Face Recognition with Illumination Variation using Subspace-Linear Discriminant Analysis
Abstract: Face recognition has
been an active research topic in the past few decades due to its potential
applications. Accurate face recognition is still a difficult task, especially
in the case that illumination is unconstrained. This paper presents an
efficient method for the recognition of faces with different illumination by
using Gabor features, which are extracted by using log-Gabor filters of six
orientations and four scales. By Using sliding window algorithm, these features
are extracted at image block-regions. Extracted features are passed to the
principal component analysis (PCA) and then to linear discriminant analysis
(LDA). For development and testing we used facial images from the Yale-B
databases. The proposed method achieved 86–100 % rank 1 recognition rate.
Author: Hendra Kusuma, Wirawan,
Adi Soeprijanto
Journal Code: jptkomputergg120032
