![]() ![]() Journal of Visual Languages and Computing 15, 3349–3361 (2009) Ramanathan, N., Chellappa, R.: Age progression in human faces: a survey. Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: A literature survey (2000) This process is experimental and the keywords may be updated as the learning algorithm improves. ![]() These keywords were added by machine and not by the authors. 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. We also demonstrate how knowing the age could improve shape-based face recognition algorithms. We then integrate the relative growth model with the Grassmann manifold and the SVM classifier. 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. In this paper, we propose a relative craniofacial growth model which is based on the science of craniofacial anthropometry. ![]() 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. Age-separated facial images usually have significant changes in both shape and texture. ![]()
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