Paper Info
Title
Laplace Graph Embedding Class Specific Dictionary Learning for Face Recognition
Abstract
AbstractThe sparse representation based classification (SRC) method and collaborative representation based classification (CRC) method have attracted more and more attention in recent years due to their promising results and robustness. However, both SRC and CRC algorithms directly use the training samples as the dictionary, which leads to a large fitting error. In this paper, we propose the Laplace graph embedding class specific dictionary learning (LGECSDL) algorithm, which trains a weight matrix and embeds a Laplace graph to reconstruct the dictionary. Firstly, it can increase the dimension of the dictionary matrix, which can be used to classify the small sample database. Secondly, it gives different dictionary atoms with different weights to improve classification accuracy. Additionally, in each class dictionary training process, the LGECSDL algorithm introduces the Laplace graph embedding method to the objective function in order to keep the local structure of each class, and the proposed method is capable of improving the performance of face recognition according to the class specific dictionary learning and Laplace graph embedding regularizer. Moreover, we also extend the proposed method to an arbitrary kernel space. Extensive experimental results on several face recognition benchmark databases demonstrate the superior performance of our proposed algorithm.
Year
DOI
Venue
2018
10.1155/2018/2179049
Periodicals
Field
DocType
Volume
Kernel (linear algebra),Facial recognition system,Dictionary learning,Laplace transform,Pattern recognition,Matrix (mathematics),Computer science,Graph embedding,Sparse approximation,Electronic engineering,Robustness (computer science),Artificial intelligence
Journal
2018
Issue
ISSN
Citations 
1
2090-0147
0
PageRank 
References 
Authors
0.34
13
3
Name
Order
Citations
PageRank
Li Wang100.34
Yanjiang Wang2158.65
Bao-Di Liu316627.34