Title
Recognition Of Noisy Facial Images Employing Transform - Domain Two-Dimensional Principal Component Analysis
Abstract
A Transform Domain Two-Dimensional Principal Component Analysis algorithm (TD2DPCA) applied to facial recognition in the presence of noise is presented. The new algorithm maintains high recognition accuracy in the presence of noise. In addition, the TD2DPCA has attractive properties with respect to storage and computational requirements. As the storage requirements are reduced by more than 90 percent, and the computational speed is reduced by a factor of two, compared with the spatial 2DPCA method. The new algorithm is applied to the ORL and Yale datasets, in the presence of salt and pepper as well as gray scale white Gaussian noise, where the Discrete Cosine transform is used. The results are given which confirm the excellent recognition accuracy of noisy facial images employing the proposed technique. © 2006 IEEE.
Publication Date
12-1-2006
Publication Title
Midwest Symposium on Circuits and Systems
Volume
1
Number of Pages
596-599
Document Type
Article; Proceedings Paper
Personal Identifier
scopus
DOI Link
https://doi.org/10.1109/MWSCAS.2006.382133
Copyright Status
Unknown
Socpus ID
34748924948 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/34748924948
STARS Citation
Abdelwahab, Moataz M. and Mikhael, Wasfy B., "Recognition Of Noisy Facial Images Employing Transform - Domain Two-Dimensional Principal Component Analysis" (2006). Scopus Export 2000s. 7695.
https://stars.library.ucf.edu/scopus2000/7695