  Human face aging is irreversible process causing changes in human face
characteristics such us hair whitening, muscles drop and wrinkles. Due to the
importance of human face aging in biometrics systems, age estimation became an
attractive area for researchers. This paper presents a novel method to estimate
the age from face images, using binarized statistical image features (BSIF) and
local binary patterns (LBP)histograms as features performed by support vector
regression (SVR) and kernel ridge regression (KRR). We applied our method on
FG-NET and PAL datasets. Our proposed method has shown superiority to that of
the state-of-the-art methods when using the whole PAL database.
