Paper Info
Title
Facial Recognition System Employing Transform Implementations of Sparse Representation Method
Abstract
A new discriminative sparse representation approach for robust face recognition via l(2) regularization (SRFR) was recently published. In this paper, a face recognition system implementation employing coefficients from two non-orthogonal transform domains, namely, Two-Dimensional Discrete Wavelet Transform (2D DWT) and 2D Discrete Cosine Transform (2D DCT), is presented. The use of these coefficients in this Mixed Wavelet Cosine Sparse Representation for Face Recognition (MWCSRFR) system as features shown to appreciably lower the computational complexity and the final storage size while maintaining the high recognition rate of the SRFR. Extensive simulations were carried out on five face databases, namely, ORL, YALE, FERET, Cropped AR, and Georgia Tech. The improved properties of the MWCSRFR are proved as shown in the given sample results.
Year
DOI
Venue
2019
10.1109/MWSCAS.2019.8885123
Midwest Symposium on Circuits and Systems Conference Proceedings
Field
DocType
ISSN
Facial recognition system,Pattern recognition,Computer science,Sparse approximation,Discrete cosine transform,Electronic engineering,Regularization (mathematics),Artificial intelligence,Discrete wavelet transform,Discriminative model,Wavelet,Computational complexity theory
Conference
1548-3746
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
Taif Alobaidi113.06
Wasfy B. Mikhael27676.27