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access icon free Personalised face neutralisation based on subspace bilinear regression

  • Author(s): Ying Chen 1, 2 ; Ruilin Bai 1 ; Chunjian Hua 3
    • View affiliations
    • Affiliations: 1: Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, People's Republic of China;
      2: Key Laboratory of System Control and Information Processing (Ministry of Education), Shanghai Jiaotong University, Shanghai 200240, People's Republic of China;
      3: School of Mechanical Engineering, Jiangnan University, Wuxi 214122, People's Republic of China
  • Source: Volume 8, Issue 4, August 2014, p. 329 – 337
    DOI:  10.1049/iet-cvi.2013.0212 , Print ISSN 1751-9632, Online ISSN 1751-9640
© The Institution of Engineering and Technology
Received 16/08/2013, Accepted 19/11/2013, Revised 20/10/2013, Published 14/01/2014

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.

Inspec keywords: face recognition; image fusion; regression analysis; image colour analysis; learning (artificial intelligence); gradient methods; Poisson equation

Other keywords: gradient differences; bilinear kernel rank reduced regression; learning-based expression neutralisation; BKRRR algorithm; grey differences; subspace bilinear regression; personalised face neutralisation; CMU multiPIE databases; PAW; expressive face recognition; piece-wise affine warp; Poisson equation; virtual neutral face; image fusion

Subjects: Other topics in statistics; Optimisation techniques; Optimisation techniques; Neural computing techniques; Computer vision and image processing techniques; Mathematical analysis; Other topics in statistics; Mathematical analysis; Sensor fusion; Image recognition

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