Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/11387
Title: Monogenic binary coding : an efficient local feature extraction approach to face recognition
Authors: Yang, M
Zhang, L 
Shiu, SCK 
Zhang, D 
Keywords: Face recognition
Gabor filtering
LBP
Monogenic binary coding
Monogenic signal analysis
Issue Date: 2012
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on information forensics and security, 2012, v. 7, no. 6, 6296709, p. 1738-1751 How to cite?
Journal: IEEE transactions on information forensics and security 
Abstract: Local-feature-based face recognition (FR) methods, such as Gabor features encoded by local binary pattern, could achieve state-of-The-art FR results in large-scale face databases such as FERET and FRGC. However, the time and space complexity of Gabor transformation are too high for many practical FR applications. In this paper, we propose a new and efficient local feature extraction scheme, namely monogenic binary coding (MBC), for face representation and recognition. Monogenic signal representation decomposes an original signal into three complementary components: amplitude, orientation, and phase. We encode the monogenic variation in each local region and monogenic feature in each pixel, and then calculate the statistical features (e.g., histogram) of the extracted local features. The local statistical features extracted from the complementary monogenic components (i.e., amplitude, orientation, and phase) are then fused for effective FR. It is shown that the proposed MBC scheme has significantly lower time and space complexity than the Gabor-transformation- based local feature methods. The extensive FR experiments on four large-scale databases demonstrated the effectiveness of MBC, whose performance is competitive with and even better than state-of-The-art local-feature-based FR methods.
URI: http://hdl.handle.net/10397/11387
ISSN: 1556-6013
EISSN: 1556-6021
DOI: 10.1109/TIFS.2012.2217332
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

56
Last Week
0
Last month
1
Citations as of Nov 10, 2017

WEB OF SCIENCETM
Citations

41
Last Week
0
Last month
0
Citations as of Nov 16, 2017

Page view(s)

59
Last Week
1
Last month
Checked on Nov 20, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.