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Design and Implementation of Resampling Techniques for Face Recognition using Classical LDA Algorithm in MATLAB
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 152 - Number 6 |
| Year of Publication: 2016 |
| Authors: Madhavi R. Bichwe, S. R Bichwe, Sugandha Satija |
10.5120/ijca2016911882
|
Madhavi R. Bichwe, S. R Bichwe, Sugandha Satija . Design and Implementation of Resampling Techniques for Face Recognition using Classical LDA Algorithm in MATLAB. International Journal of Computer Applications. 152, 6 ( Oct 2016), 28-32. DOI=10.5120/ijca2016911882
Abstract
A number of applications require robust human face recognition under varying environmental lighting conditions and different facial expressions, which considerably vary the appearance of human face. However, in many face recognition applications, only a small number of training samples for each subject are available; these samples are not able to capture all the facial appearance variations, to utilize the resampling techniques to generate several subsets of samples from the original training dataset. A classic appearance-based recognizer, LDA-based classifier, is applied to each of the generated subsets to construct a LDA representation for face recognition. The discrimination power of various human facial features is studied and a new scheme for automatic face recognition (AFR) is proposed. This paper focuses on the linear discriminant analysis (LDA) of different aspects of human faces in the spatial as well as in the wavelet domain. The LDA of faces also provides us with a small set of features that carry the most relevant information for classification purposes. The features are obtained through eigenvector analysis of scatter matrices with the objective of maximizing between-class variations and minimizing within-class variations. The result is an efficient projection-based feature-extraction and classification scheme for AFR. Multisource data analysis are used to provide more reliable recognition results. For a medium-sized database of human faces, excellent classification accuracy is achieved with the use of very-low-dimensional feature vectors. Moreover, the method is applicable to many other image-recognition tasks.
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