Title

Supervised Facial Recognition Based On Multi-Resolution Analysis And Feature Alignment

Abstract

A new supervised algorithm for face recognition based on the integration of Two-Dimensional Discrete Multiwavelet Transform (2-D DMWT), 2-D Radon Transform, and 2-D Discrete Wavelet Transform (2-D DWT) is proposed1. In the feature extraction step, Multiwavelet filter banks are used to extract useful information from the face images. The extracted information is then aligned using the Radon Transform, and localized into a single band using 2-D DWT for efficient sparse data representation. This information is fed into a Neural Network based classifier for training and testing. The proposed method is tested on three different databases, namely, ORL, YALE and subset fc of FERET, which comprise different poses and lighting conditions. It is shown that this approach can significantly improve the classification performance and the storage requirements of the overall recognition system.

Publication Date

9-23-2014

Publication Title

Midwest Symposium on Circuits and Systems

Number of Pages

137-140

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/MWSCAS.2014.6908371

Socpus ID

84908478806 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84908478806

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