Estimating human age by manifold analysis of face pictures and regression on aging features

Extensive recent studies on human faces reveal significant potential applications of automatic age estimation via face image analysis. Due to the temporal features of age progression, aging face images display sequential pattern of low-dimensional distribution. Through manifold analysis of face pictures, we developed a novel age estimation framework. The manifold learning methods are applied to find a sufficient embedding space and model the low-dimensional manifold data with a multiple linear regression function. Experimental results on a large size age database demonstrate the effectiveness of the framework. To our best knowledge, this is the first work involving the manifold ways of age estimation.