Facial age estimation using BSIF and LBP
This work addresses age estimation for biometric systems, but it is incremental as it builds on existing feature extraction and regression techniques.
The paper tackles facial age estimation by combining binarized statistical image features (BSIF) and local binary patterns (LBP) with support vector regression (SVR) and kernel ridge regression (KRR), achieving superior performance to state-of-the-art methods on the PAL dataset.
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.