Personalised‐face neutralisation using best‐matched face shape with a neutral‐face database

Abstract

Conventional personalised‐face neutralisation methods use facial‐expression databases; however, the database creation and maintenance is a tedious process, and should be minimised. Moreover, face‐shape template should be also considerably used due to its crucial factor. This study proposes a personalised‐face neutralisation method using best‐matched face‐shape template with neutral‐face database. In personalised‐face neutralisation, the best‐matched face‐shape template which is assumed as the most similar to the neutralisation expression face is found based on coarse‐to‐fine concept, and used for warping textures. Additionally, closed eyes are detected and opened up by using eye shape of the best‐matched face shape, and mixed intensities of original closed‐eye and the best‐matched one. To evaluate the performance of the proposed method, experiments were performed using the CMU Multi‐PIE database and the results reveal that the proposed method reduces gradient mean square error 0.07% on average and improves face recognition accuracy by 1.13% approximately comparing with the conventional method, while requiring only a single neutral database without expression images.

Keywords

• personalised-face neutralisation method
• best-matched face-shape template
• neutral-face database
• facial-expression databases
• database creation
• database maintenance