Abstract
Automatic face age estimation is a challenging task due to its complexity owing to genetic difference, behavior and environmental factors, and also the dynamics of facial aging between different individuals. In this paper, we propose a feature fusion method to estimate the face age via SVR, which ensembles global feature from Active Appearance Model (AAM) and the local feature from Gabor wavelet transformation. Our experimental results on UIUC-PAL database show that our proposed method works well. © 2011 Springer-Verlag.
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Yang, W., Chen, C., Ricanek, K., & Sun, C. (2011). Ensemble of global and local features for face age estimation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6676 LNCS, pp. 251–259). https://doi.org/10.1007/978-3-642-21090-7_30
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