Supervised Facial Recognition Based On Eigenanalysis Of Multiresolution And Independent Features

Abstract

In this paper, a supervised facial recognition system is presented. In the feature extraction step, a Two Dimensional Discrete Multiwavelet Transform (2D DMWT) is used to extract useful information from the face images. The 2D DMWT is followed by a Two-Dimensional Fast Independent Component Analysis (2D FastICA) and eigendecomposition to obtain discriminating and independent features. The resulting compressed features are fed into a Neural Network (NNT) based classifier for training and testing. All techniques are tested using ORL, YALE, and FERET databases. The proposed approach shows a significant improvement in the recognition rate, storage requirements, as well as computational complexity.

Publication Date

9-28-2015

Publication Title

Midwest Symposium on Circuits and Systems

Volume

2015-September

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/MWSCAS.2015.7282087

Socpus ID

84962109860 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84962109860

This document is currently not available here.

Share

COinS