Supervised Facial Recognition Based On Multiresolution Analysis With Radon Transform

Abstract

A new supervised facial recognition approach based on the integration of Two Dimensional Discrete Multiwavelet Transform (2D DMWT), 2D Radon Transform (2D RT), and 3D DWT is proposed. In the feature extraction step, 2D DMWT is used to extract the useful information from the image. The extracted features are then aligned using 2D RT and localized in one single band using 3D DWT. The resulting features are fed into a Neural Network for both training and testing. The proposed algorithm is tested on different databases, namely, ORL, YALE, and subset fc of FERET. It is shown that the proposed approach can significantly improve the recognition rate and the storage requirements of the overall recognition system.

Publication Date

4-24-2015

Publication Title

Conference Record - Asilomar Conference on Signals, Systems and Computers

Volume

2015-April

Number of Pages

928-932

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/ACSSC.2014.7094589

Socpus ID

84940501609 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84940501609

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