Paper Info
Title
Research of SWNMF with New Iteration Rules for Facial Feature Extraction and Recognition.
Abstract
Weighted nonnegative matrix factorization (WNMF) is a technology for feature extraction, which can extract the feature of face dataset, and then the feature can be recognized by the classifier. To improve the performance of WNMF for feature extraction, a new iteration rule is proposed in this paper. Meanwhile, the base matrix U is sparse based on the threshold, and the new method is named sparse weighted nonnegative matrix factorization (SWNMF). The new iteration rules are based on the smaller iteration steps, thus, the search is more precise, therefore, the recognition rate can be improved. In addition, the sparse method based on the threshold is adopted to update the base matrix U, which can make the extracted feature more sparse and concentrate, and then easier to recognize. The SWNMF method is applied on the ORL and JAFEE datasets, and from the experiment results we can find that the recognition rates are improved extensively based on the new iteration rules proposed in this paper. The recognition rate of new SWNMF method reached 98% for ORL face database and 100% for JAFEE face database, respectively, which are higher than the PCA method, the sparse nonnegative matrix factorization (SNMF) method, the convex non-negative matrix factorization (CNMF) method and multi-layer NMF method.
Year
DOI
Venue
2019
10.3390/sym11030354
SYMMETRY-BASEL
Keywords
Field
DocType
face recognition,new additive iteration rule,threshold sparse,base matrix,coefficient matrix,weighted nonnegative matrix factorization
Facial recognition system,Combinatorics,Coefficient matrix,Pattern recognition,Matrix (mathematics),Matrix decomposition,Feature extraction,Regular polygon,Artificial intelligence,Non-negative matrix factorization,Classifier (linguistics),Mathematics
Journal
Volume
Issue
Citations 
11
3
0
PageRank 
References 
Authors
0.34
0
1
Name
Order
Citations
PageRank
jing zhou111220.35