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Content Provider | IET Digital Library |
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Author | Chen, Ying Bai, Ruilin Hua, Chunjian |
Abstract | Expression face neutralisation helps to improve the performance of expressive face recognition with one single neutral sample in gallery per subject. For learning-based expression neutralisation, the virtual neutral face totally relies on training samples, which removes person-specific characters from the neutralised face. Bilinear kernel rank reduced regression (BKRRR) algorithm is designed in a virtual subspace to simultaneously and efficiently generate both virtual expressive and neutral images from training samples. An expression mask is then established using grey and gradient differences of the two images. The test expression image is transformed to neutral template by piece-wise affine warp (PAW). Using the virtual BKRRR neutral image as source, the PAW image as destination and the area covered by expression mask as clone area, an image fusion strategy based on Poisson equation is then designed, which achieves virtual neutralised face image with person-specific characters preserved. From experiments on the CMU Multi-PIE databases, it could be observed that the neutral faces synthesised by the proposed method could effectively approximate the real ground truth expressive faces, and greatly improve the performance of classic face recognition algorithms on expression variant problems. |
Starting Page | 329 |
Ending Page | 337 |
Page Count | 9 |
ISSN | 17519632 |
Volume Number | 8 |
e-ISSN | 17519640 |
Issue Number | Issue 4, Aug (2014) |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/iet-cvi/8/4 |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2013.0212 |
Journal | IET Computer Vision |
Publisher Date | 2014-01-14 |
Access Restriction | Open |
Rights Holder | © The Institution of Engineering and Technology |
Subject Keyword | Bilinear Kernel Rank Reduced Regression BKRRR Algorithm CMU MultiPIE Databases Computer Vision And Image Processing Technique Expressive Face Recognition Face Recognition Gradient Differences Gradient Method Grey Differences Image Colour Analysis Image Fusion Image Recognition Learning in AI Learning-based Expression Neutralisation Mathematical Analysis Neural Computing Technique Optimisation Technique PAW Personalised Face Neutralisation Piece-wise Affine Warp Poisson Equation Regression Analysis Sensor Fusion Statistics SubSpace Bilinear Regression Virtual Neutral Face |
Content Type | Text |
Resource Type | Article |
Subject | Computer Vision and Pattern Recognition Software |
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