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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Zhen Lei Dong Yi Li, S.Z. |
| Copyright Year | 2014 |
| Description | Author affiliation: Center for Biometrics & Security Res. & Nat. Lab. of Pattern Recognition, Inst. of Autom., Beijing, China (Zhen Lei; Dong Yi; Li, S.Z.) |
| Abstract | LBP is an effective descriptor for face recognition. LBP encodes the ordinal relationship between the neighborhood samplings and the central one to obtain robust face representation. However, additional information like the difference among neighboring pixels, which may be helpful for face recognition, is ignored. On the other hand, gradient information which enhances the edge response and suppresses the external noise like illumination variation, is usually useful for face recognition. In this paper, we propose a novel face descriptor, namely local gradient order pattern (LGOP), taking into account the ordinal relationship of gradient responses in local region to obtain robust face representation. After pattern encoding, a 2-D histogram is consequently adopted to calculate the occurrence frequency of different patterns and multi-scale histogram features are extracted to represent the face image. We further adopt whitened principal component analysis (WPCA) to reduce the feature dimensionality and improve the computational efficiency. Extensive experiments on FERET, CAS-PEAL and LFW validates the effectiveness of LGOP for both constrained and unconstrained face recognition problems. |
| Starting Page | 387 |
| Ending Page | 392 |
| File Size | 780698 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781479952090 |
| ISSN | 10514651 |
| DOI | 10.1109/ICPR.2014.75 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-08-24 |
| Publisher Place | Sweden |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Face Face recognition Histograms Feature extraction Databases Robustness Lighting |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition |
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