Paper Info
Title
Supervised bilateral two-dimensional locality preserving projection algorithm based on Gabor wavelet.
Abstract
Bilateral two-dimensional locality preserving projection (B2DLPP) is an effective method for unsupervised linear dimensionality reduction, which directly extracts face features from image matrices based on locality criterion. Motivated by B2DLPP, this paper proposes a supervised bilateral two-dimensional locality preserving projection (SB2DLPP). Different from B2DLPP, the proposed method takes into account the class information when constructing the similarity matrix. It increases inter-class distance in the projection space so that better right and left-projection matrices are obtained. Furthermore, a Gabor-based supervised bilateral two-dimensional locality preserving projection method is proposed for face recognition. Gabor wavelet representations are adopted for face images to make the proposed method robust to illumination variations and facial expression changes. Then, SB2DLPP is applied to reduce feature dimension. The performance of the proposed method is evaluated and compared with other traditional face recognition schemes on the FERET, Yale and JAFFE databases. The experiment results demonstrate the effectiveness and superiority of the proposed approach.
Year
DOI
Venue
2016
10.1007/s11760-016-0950-1
Signal, Image and Video Processing
Keywords
Field
DocType
Face recognition, Bilateral two-dimensional locality preserving projection, Gabor wavelets, Supervised learning
Facial recognition system,Computer vision,Locality,Dimensionality reduction,Pattern recognition,Dykstra's projection algorithm,Gabor wavelet,Projection method,Supervised learning,Artificial intelligence,Mathematics,Feature Dimension
Journal
Volume
Issue
ISSN
10
8
1863-1711
Citations 
PageRank 
References 
3
0.37
11
Authors
4
Name
Order
Citations
PageRank
Jiuzhen Liang130.37
Zhenjie Hou2132.30
Chen Chen399750.53
Xiuxiu Xu450.73