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access icon free Facial aging simulation via tensor completion and metric learning

© The Institution of Engineering and Technology
Received 06/03/2016, Accepted 12/09/2016, Revised 29/08/2016, Published 15/09/2016

Facial aging simulation is one of the most challenging issues in automatic machine based face analysis, where the most essential requirements are (i) human identity should remain stable in texture synthesis and (ii) the texture synthesised is expected to accord with human cognitive perception in aging. In this study, the authors propose a tensor completion based method to transform the simulation task to a standard matrix completion one. To protect human dependent characteristics during texture synthesis, the proposed method processes the two major components, i.e. identity and age, in different channels. Furthermore, they incorporate prior information in such a process, assuming that the textures of different subjects in the same age group are similar and similar looking people tend to age in similar ways, and the metric learning technique is adopted to measure the similarity between identities so that the faces that have the highest similarities with the one in the test image are assigned bigger weights in texture generation. In addition, shape deformation is also considered to make the synthesised images more natural. Experimental results achieved on the FG-NET database demonstrate the effectiveness of the proposed method.

Inspec keywords: learning (artificial intelligence); face recognition; digital simulation; image texture; tensors; matrix algebra

Other keywords: standard matrix completion; facial aging simulation; FG-NET database; tensor completion based method; metric learning; texture synthesis; human identity; similarity measurement; shape deformation; texture generation; automatic machine based face analysis; human cognitive perception

Subjects: Image recognition; Knowledge engineering techniques; Algebra; Computer vision and image processing techniques; Algebra

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