Paper Info
Title
Semi-supervised local ridge regression for local matching based face recognition.
Abstract
In this paper, a novel algorithm named Semi-supervised Local Ridge Regression (SSLRR) is proposed for local matching based face recognition. Compared with other algorithms, the proposed algorithm possesses two advantages. Firstly, SSLRR utilizes a multiple graph based semi-supervised technique to propagate the class labels of labeled samples to the unlabeled ones. Thus, the information of both labeled and unlabeled data can be employed in our algorithm to improve its performance. Secondly, unlike most local matching based face recognition algorithms which assume different sub-images from the same face are independent, an adaptive non-negative weight vector is introduced into our SSLRR to combine the Laplacian matrices obtained by different sub-images. Therefore, the latent complementary information of multiple sub-patterns from the same face image can be taken into account. Moreover, a simple yet efficient iterative update scheme is also proposed to solve our SSLRR model. Extensive experiments are performed on five standard face databases (Yale, Extended YaleB, AR, CMU PIE and LFW) to demonstrate the efficiency of the proposed algorithm. Experimental results show that SSLRR obtains better recognition performance than some other state-of-the-art approaches.
Year
DOI
Venue
2015
10.1016/j.neucom.2015.04.085
Neurocomputing
Keywords
Field
DocType
Ridge regression,Semi-supervised learning,Semi-supervised local ridge regression,Local matching based face recognition
Facial recognition system,Semi-supervised learning,Pattern recognition,Regression,Matrix (mathematics),Computer science,Weight,Local matching,Ridge,Artificial intelligence,Machine learning,Laplace operator
Journal
Volume
Issue
ISSN
167
C
0925-2312
Citations 
PageRank 
References 
6
0.42
36
Authors
5
Name
Order
Citations
PageRank
Yugen Yi19215.25
Chao Bi260.75
Xiaohui Li360.42
Jianzhong Wang421417.72
Jun Kong515814.14