An Effective L0 - SVM Classifier For Face Recognition Based on Haar Feature

Yunpeng WANG, Xiaogang XIA

Abstract


Face recognition is an important research topic in pattern recognition, and in which, it is a striking direction that how to extract the useful features to express face. In this paper, we present a technique for face recognition by L0 -SVM classifier based on Haar features. Firstly, a mass of Haar features are produced by different kinds of Haar template. Then basing on the Haar features and according to the DC algorithm, L0-SVM classifier is constructed in order to enhance computational and time efficiency, as well as its validity is proved in theory. Finally, experimental results on databases show that the method can effectively improve the recognition rate of the face with a small scale of samples.

Keywords


Face images; Haar features; L0-SVM classifier; DC programming

Full Text:

PDF

References


Le Hoai, M., Le Thi, H. A., Pham Dinh, T., & Huynh, V. N. (2013). Block clustering based on difference of convex functions (DC) programming and DC algorithms. Neural Computation, 259-278.

Le Thi, H. A., & Pham Dinh, T. (1997). Solving a class of linearly constrained indefinite quadratic problems by DC algorithms. Journal of Global Optimization, 2776-2807.

Le Thi, H. A., & Pham Dinh, T. (2005). The DC (difference of convex functions) programming and DCA revisited with DC models of real world nonconvex optimization problems. Annals of Operations Research, 23-46.

Lienhart, R., & Maydt, J. (2002). An extended set of Haar-like features for rapid object detection. The IEEE International Conference on Image Processing, 1, 900-903.

Thiao, M., Pham Dinh, T., & Le Thi, H. A. (2008). DC programming approach for a class of nonconvex programs involving -norm. Modelling, Computation and Optimization in Information Systems and Management Sciences, 14, 358-367.

Viola, P., & Jones, M. (2001). Rapid object detection using a boosted cascade of a simple features (pp.511-518). In proceeding of International Conference on Computer vision and Pattern Recognition.

Zhang, H., A., Berg, M., Maire, & Malik, J. (2006). SVM-KNN: Discriminative nearest neighbor classification for visual category recognition. Computer Vision and Pattern Recognition, 2126-2136.




DOI: http://dx.doi.org/10.3968/%25x

DOI (PDF): http://dx.doi.org/10.3968/%25x

Refbacks

  • There are currently no refbacks.


Copyright (c) 2016 Advances in Natural Science




Share us to:   


Reminder

How to do online submission to another Journal?

If you have already registered in Journal A, then how can you submit another article to Journal B? It takes two steps to make it happen:

1. Register yourself in Journal B as an Author

Find the journal you want to submit to in CATEGORIES, click on “VIEW JOURNAL”, “Online Submissions”, “GO TO LOGIN” and “Edit My Profile”. Check “Author” on the “Edit Profile” page, then “Save”.

2. Submission

Go to “User Home”, and click on “Author” under the name of Journal B. You may start a New Submission by clicking on “CLICK HERE”.

We only use the following emails to deal with issues about paper acceptance, payment and submission of electronic versions of our journals to databases:
caooc@hotmail.com; office@cscanada.net; office@cscanada.org
ans@cscanada.net;ans@cscanada.org

 Articles published in Advances in Natural Science are licensed under Creative Commons Attribution 4.0 (CC-BY).

 ADVANCES IN NATURAL SCIENCE Editorial Office

Address: 1055 Rue Lucien-L'Allier, Unit #772, Montreal, QC H3G 3C4, Canada.

Telephone: 1-514-558 6138
Website: Http://www.cscanada.net; Http://www.cscanada.org
E-mail:caooc@hotmail.com; office@cscanada.net

Copyright © 2010 Canadian Research & Development Centre of Sciences and Cultures