http://iet.metastore.ingenta.com
1887

Application of MEEMD in post-processing of dimensionality reduction methods for face recognition

Application of MEEMD in post-processing of dimensionality reduction methods for face recognition

For access to this article, please select a purchase option:

Buy eFirst article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Biometrics — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Dimensionality reduction techniques are powerful tools for face recognition, because they obtain important information from a dataset. Several dimensionality reduction methods proposed in literature have been improved thanks to pre-processing approaches. However, they also require post-processing to rectify and increase the quality of projected data. This study presents a simple and new discriminative post-processing framework to make the dimensionality reduction methods robust to outliers. In detail, the proposed approach separates features according to their scale using multidimensional ensemble empirical mode decomposition (MEEMD) and then the spatial and frequency domain processing methods are employed to preserve crucial features. The performance of the proposed method is evaluated on ORL, Extended Yale B, AR, and LFW datasets by several dimensionality reduction techniques. The experimental results demonstrate that the proposed algorithm can perform very well in face recognition.

http://iet.metastore.ingenta.com/content/journals/10.1049/iet-bmt.2018.5033
Loading

Related content

content/journals/10.1049/iet-bmt.2018.5033
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address