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Content Provider | IET Digital Library |
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Author | xian, Yang Hui yong, Cai Yong |
Abstract | To overcome the limitation of traditional illumination invariant methods for single sample face recognition, a modified version of gradientface named adaptively weighted orthogonal gradient binary pattern (AWOGBP), which is proved robust to illumination variation, is proposed in this study. First, the Tetrolet transform is performed on the images to obtain low frequency and high frequency components and the retina model processing is applied to low frequency component to make the image more robust to illumination, in the meantime, the authors multiply each element in high frequency components with a scale factor to accentuate details. Then the proposed AWOGBP is used to get the feature vectors of each direction and all the feature vectors are concatenated into the general feature vector for face recognition with the weights of the sub-graph based on their information entropy which is defined as the contribution to describe the whole face images. Finally the principle component analysis method is used to reduce dimensions and the nearest neighbour classifier is used for face image classification and recognition. Experimental results on CMU PIE and Extended Yale B face databases indicate that the proposed method is significantly better as compared with related state-of-the-art methods. |
Starting Page | 76 |
Ending Page | 82 |
Page Count | 7 |
ISSN | 20474938 |
Volume Number | 5 |
e-ISSN | 20474946 |
Issue Number | Issue 2, Jun (2016) |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/iet-bmt/5/2 |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/iet-bmt.2014.0082 |
Journal | IET Biometrics |
Publisher Date | 2016-06-01 |
Access Restriction | Open |
Rights Holder | © The Institution of Engineering and Technology |
Subject Keyword | Adaptively Weighted Orthogonal Gradient Binary Pattern Algebra AWOGBP Combinatorial Mathematics Computer Vision And Image Processing Technique Face Image Classification Face Recognition Feature Extraction Feature Vector Gradient Method Graph Theory Illumination Variation Image Classification Image Recognition Information Entropy Integral Transforms Interpolation And Function Approximation Numerical Analysis Scale Factor Subgraph Weight Tetrolet Transform Transform Vector |
Content Type | Text |
Resource Type | Article |
Subject | Signal Processing Computer Vision and Pattern Recognition Software |
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