Paper Info
Title
Rotation and noise invariant near-infrared face recognition by means of Zernike moments and spectral regression discriminant analysis
Abstract
Face recognition is a rapidly growing research area, which is based heavily on the methods of machine learning, computer vision, and image processing. We propose a rotation and noise invariant near-infrared face-recognition system using an orthogonal invariant moment, namely, Zernike moments (ZMs) as a feature extractor in the near-infrared domain and spectral regression discriminant analysis (SRDA) as an efficient algorithm to decrease the computational complexity of the system, enhance the discrimination power of features, and solve the "small sample size" problem simultaneously. Experimental results based on the CASIA NIR database show the noise robustness and rotation invariance of the proposed approach. Further analysis shows that SRDA as a sophisticated technique, improves the accuracy and time complexity of the system compared with other data reduction methods such as linear discriminant analysis. (C) 2013 SPIE and IS&T
Year
DOI
Venue
2013
10.1117/1.JEI.22.1.013030
JOURNAL OF ELECTRONIC IMAGING
Field
DocType
Volume
Computer vision,Facial recognition system,Pattern recognition,Computer science,Discriminant,Image processing,Zernike polynomials,Robustness (computer science),Artificial intelligence,Linear discriminant analysis,Time complexity,Computational complexity theory
Journal
22
Issue
ISSN
Citations 
1
1017-9909
10
PageRank 
References 
Authors
0.50
16
6
Name
Order
Citations
PageRank
Sajad Farokhi1464.35
Siti Mariyam Shamsuddin239841.80
J. Flusser339825.42
Usman Ullah Sheikh4498.41
Mohammad Khansari5252.09
kourosh jafarikhouzani626128.87