Face age estimation using model selection

Face age estimation is a difficult problem due to the dynamics of facial aging and its complex interactions owing to genetics and behavior factors. In this work we develop a robust age estimation system tuned by model selection that outperforms all prior systems on the FG-NET face database. We study various model selection methods systematically to determine the best selection methods among Least Angle Regression (LAR), Principle Component Analysis (PCA), and Locality Preserving Projections (LPP) for age estimation. Our performance analysis on PAL and FG-NET databases suggest that age estimation with LAR or LPP outperforms the full feature model. Furthermore, this work develops a novel operator named “graph age preserving” (GAP) to build a neighborhood graph for LPP for age estimation.