Paper Info
Title
MMI-Based Optimal LBP Code Selection for Face Recognition
Abstract
Many variants of local binary patterns (LBPs) are widely used for face analysis due to their inherent simplicity and robustness. However, it has not yet been proven that LBPsare optimal for this task in regards to achieving the best balance between minimizing code numbers and reducing classification error. We propose an effective code selection method for selecting optimal LBP (OLBP) based on the maximization of mutual information (MMI) between features and class labels. We demonstrate the effectiveness of the proposed OLBP through several face recognition experiments. Experimental results show that the OLBP outperforms other features such as LBP, ULBP, and MCT in terms of minimizing the number of codes and reducing the classification error.
Year
DOI
Venue
2009
10.1109/ISM.2009.121
ISM
Keywords
Field
DocType
optimisation,mmi-based optimal lbp code,feature,ulbp,face recognition,lbpsare,olbp,mutual information maximization,face recognition experiment,optimal lbp,effective code selection method,lbp,face analysis,feature extraction,mct,mmi,code number,proposed olbp,local binary patterns,class label,feature selection,classification error,mmi-based optimal lbp code selection,lbpsare optimal,best balance,databases,lighting,local binary pattern,face,mutual information,pixel
Facial recognition system,Feature selection,Pattern recognition,Computer science,Local binary patterns,Robustness (computer science),Feature extraction,Mutual information,Pixel,Artificial intelligence,Maximization,Machine learning
Conference
ISBN
Citations 
PageRank 
978-0-7695-3890-7
0
0.34
References 
Authors
21
3
Name
Order
Citations
PageRank
TaeWan Kim110917.07
Jongmin Yoon2153.36
Daijin Kim31882126.85