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.

A Review of Methodologies in Machine Learning For Face Shape Analysis

“…This method achieved high position accuracy at a low cost by merging shape extraction using separable filters with pattern matching rooted on the subspace method. Automatic hairstyle recommendation in a face image is recommended to automatically tell about how a person looks while wearing the selected hairstyle [11]. It uses the Multi-kernel learning system and Vector concatenation methods.…”