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

Sparsifying transform learning for face image classification

Sparsifying transform learning for face image classification

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

Buy article PDF
$19.95
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(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:
 
 
 
 
 
Electronics Letters — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Sparse signal representation showed promising results in the field of face recognition in the past few years. An algorithm based on a sparsifying transform is considered. It mainly learns a dictionary that can transform the image into sparse vectors. In the transformation domain, the images of the same class should have similar non-zero coefficients pattern that can be used for identification. The classification process of this method only requires to transform the image and make norm comparisons to determine the class of the image. The proposed method shows a comparable performance with the other known methods in the literature by means of accuracy. A novel method in sparsity-based image identification that uses analysis dictionaries is proposed, unlike the conventional sparsity-based methods. One advantage of the proposed algorithm is the low computational cost of the classification process.

References

    1. 1)
    2. 2)
      • 2. Cortes, C., Vapnik, V.: ‘Support-vector networks’, Mach. Learn., 1995, 20, pp. 273297.
    3. 3)
    4. 4)
    5. 5)
    6. 6)
    7. 7)
    8. 8)
    9. 9)
      • 9. Samaria, F.S., Harter, A.C.: ‘Parameterisation of a stochastic model for human face identification’. Proc. 1994 IEEE Workshop on Applications of Computer Vision, Sarasota, FL, USA, December 1994, pp. 138142.
    10. 10)
      • 10. Martinez, A., Benavente, R.: ‘The AR face database’, Technical Report 24, Computer Vision Center, June 1998.
    11. 11)
http://iet.metastore.ingenta.com/content/journals/10.1049/el.2018.0524
Loading

Related content

content/journals/10.1049/el.2018.0524
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Correspondence
This article has following corresponding article(s):
in brief
This is a required field
Please enter a valid email address