Face recognition using a new compressive sensing-based feature extraction method

Face recognition using a new compressive sensing-based feature extraction method This paper proposes a novel face recognition algorithm that utilizes a sparse Fast Fourier Transform (FFT)-based feature extraction method. In our algorithm, we use Compressive Sampling (CS) theory two times. First, in the feature extraction process for extracting the feature vectors from a face images, and second, in the classification process where the CS reconstruction is used for selecting true classes. As a result, a significant reduction in the dimensionality of the signals is achieved. Extensive and comparative experiments have been conducted to evaluate the performance of the proposed scheme. The experiment results show that the combined Compressive Sensing and Sparse Representation Classification (SRC) achieves a high recognition accuracy, while maintaining a reasonable computational complexity. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Multimedia Tools and Applications Springer Journals

Face recognition using a new compressive sensing-based feature extraction method

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Publisher
Springer Journals
Copyright
Copyright © 2017 by Springer Science+Business Media, LLC
Subject
Computer Science; Multimedia Information Systems; Computer Communication Networks; Data Structures, Cryptology and Information Theory; Special Purpose and Application-Based Systems
ISSN
1380-7501
eISSN
1573-7721
D.O.I.
10.1007/s11042-017-5007-0
Publisher site
See Article on Publisher Site

Abstract

This paper proposes a novel face recognition algorithm that utilizes a sparse Fast Fourier Transform (FFT)-based feature extraction method. In our algorithm, we use Compressive Sampling (CS) theory two times. First, in the feature extraction process for extracting the feature vectors from a face images, and second, in the classification process where the CS reconstruction is used for selecting true classes. As a result, a significant reduction in the dimensionality of the signals is achieved. Extensive and comparative experiments have been conducted to evaluate the performance of the proposed scheme. The experiment results show that the combined Compressive Sensing and Sparse Representation Classification (SRC) achieves a high recognition accuracy, while maintaining a reasonable computational complexity.

Journal

Multimedia Tools and ApplicationsSpringer Journals

Published: Jul 10, 2017

References

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