Face Recognition using LDA based Support Vector Machine

Authors

  • Ujjal Suttra Dhar
  • Md. Rabiul Islam
  • Rizoan Toufiq

Abstract

Abstract
Recognition of a person is an easy task for human brains. From different experiments, we have found that even one to three days old babies are able to distinguish between known faces. So, it is very difficult task for a computer to recognize an unknown human face although a computer has been trained with a huge set of human face. Now-a-days face recognition has received significant attention of researchers. Lots of improvements have been obtained in successful face recognition. Support Vector Machine is a method to classify things, which is implemented successfully on face recognition. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are basic method, widely used in feature extraction of faces. This paper presents an approach to face recognition system using Support Vector Machine with LDA based facial features. Two
large facial databases have been used to measure the performance of the system. Linear Discriminant Analysis is applied to find aspects of faces, which are important for identification and verification. It is considered that large eigen values are adequate in face recognition system. LDA is used for dimension reduction, which is done by selecting eigenfaces of nth largest eigenvalues. Finally, these eigenfaces are projected onto face space and used for training in classification using SVM classifier.

Keywords: Face recognition, support vector machine, linear discriminant analysis, facial feature vector

Downloads

Published

2019-07-09

Issue

Vol. 5 No. 2 (2014)

Section

Articles

License

Declaration and Copyright Transfer Form

(to be completed by authors)

I/ We, the undersigned author(s) of the submitted manuscript, hereby declare, that the above manuscript which is submitted for publication in the STM Journals(s), is not published already in part or whole (except in the form of abstract) in any journal or magazine for private or public circulation, and, is not under consideration of publication elsewhere.

  • I/We will not withdraw the manuscript after 1 week of submission as I have read the Author Guidelines and will adhere to the guidelines.
  • I/We Author(s ) have niether given nor will give this manuscript elsewhere for publishing after submitting in STM Journal(s).
  • I/ We have read the original version of the manuscript and am/ are responsible for the thought contents embodied in it. The work dealt in the manuscript is my/ our own, and my/ our individual contribution to this work is significant enough to qualify for authorship.
  •  I/We also agree to the authorship of the article in the following order:

Author’s name                                    

 

1. ________________

2. ________________

3. ________________

4. ________________

 

We  Author(s) tick this box and would request you to consider it as our signature as we agree to the terms of this Copyright Notice, which will apply to this submission if and when it is published by this journal.