Abstract
In this work we aim to study rigorously the facial age estimation in a multiethnic environment with 39 possible combination of four feature normalization methods, two simple feature fusion methods, two feature selection methods, and three face representation methods as Gabor, AAM and LBP. First, Gabor feature is extracted as facial representation for age estimation. Inspired by [3], we further fuse the global Active Appearance Model (AAM) and the local Gabor features as the representation of faces. Combining with feature selection schemes such as Least Angle Regression (LAR) and sequential selection, an advanced age estimation system is proposed on the fused features. Systematic comparative of 39 experiments demonstrate that (1) As a single facial representation, Gabor features surprisedly outperform LBP features or even AAM features. (2) With global/local feature fusion scheme, fused Gabor and AAM or fused LBP and AAM features can achieve significant improvement in age estimation over single feature representation alone.
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Chen, C., Yang, W., Wang, Y., Shan, S., Ricanek, K. (2011). Learning Gabor Features for Facial Age Estimation. In: Sun, Z., Lai, J., Chen, X., Tan, T. (eds) Biometric Recognition. CCBR 2011. Lecture Notes in Computer Science, vol 7098. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25449-9_26
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DOI: https://doi.org/10.1007/978-3-642-25449-9_26
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