Age Invariant Face Verification with Relative Craniofacial Growth Model

Age-separated facial images usually have significant changes in both shape and texture. Although many face recognition algorithms have been proposed in the last two decades, the problem of recognizing facial images across aging remains an open problem. In this paper, we propose a relative craniofacial growth model which is based on the science of craniofacial anthropometry. Compared to the traditional craniofacial growth model, the proposed method introduces a set of linear equations on the relative growth parameters which can be easily applied for facial image verification across aging. We then integrate the relative growth model with the Grassmann manifold and the SVM classifier. We also demonstrate how knowing the age could improve shape-based face recognition algorithms. Experiments show that the proposed method is able to mitigate the variations caused by the aging progress and thus effectively improve the performance of open-set face verification across aging.

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