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A Transform Domain Implementation of Sparse Representation Method for Robust Face Recognition

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Abstract

Recently, a new discriminative sparse representation method for robust face recognition via \(\textit{l}_2\) regularization (NDSRFR) was reported. In this contribution, a transform domain (TDNDSRFR) is presented. The discrete cosine transform implementation of the TDNDSRFR is given and shown to maintain the recognition accuracy of the NDSRFR while yielding considerable reduction in the computational complexity and storage requirements. Also, a technique that selects the balance parameter is introduced. Extensive simulations were performed on six face databases, namely, ORL, YALE, FERET, FEI, Cropped AR, and Georgia Tech, and sample results are given which confirm the enhanced properties of the TDNDSRFR.

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Acknowledgements

This work was supported by the Iraqi government scholarship (HCED). The authors acknowledge the University of Central Florida Advanced Research Computing Center for providing computational resources that contributed to results reported herein. https://arcc.ist.ucf.edu. Also, the authors would like to thank Mr. André Beckus for his valuable comments.

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  1. Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL, 32816, USA

    Taif Alobaidi & Wasfy B. Mikhael

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  1. Taif Alobaidi
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  2. Wasfy B. Mikhael
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Correspondence to Taif Alobaidi.

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Alobaidi, T., Mikhael, W.B. A Transform Domain Implementation of Sparse Representation Method for Robust Face Recognition. Circuits Syst Signal Process 38, 4302–4313 (2019). https://doi.org/10.1007/s00034-019-01099-w

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