Abstract -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.
In previous studies, an algorithm for extracting handcrafted features was used for age estimation such as binarized statistical image features (BSIFs) and local binary pattern (LBP) features by Bekhouche et al. REF .
Facial age estimation using BSIF and LBP
ArXiv
ArXiv
Computer Science