Facial age estimation using BSIF and LBP

Facial age estimation using BSIF and LBP

Overview of the system


Salah Eddine Bekhouche1, Abdelkrim Ouafi1, Abdelmalik Taleb-Ahmed2, Abdenour Hadid3, Azeddine Benlamoudi1

  1. Laboratory of LESIA, University of Biskra, Algeria
  2. Laboratory of LAMIH, University of Valenciennes, France
  3. Center for Machine Vision Research, University of Oulu, Finland


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.

Please cite as:

Title = {Facial age estimation using BSIF and LBP},
Author = {Bekhouche, S. and Ouafi, A. and Taleb-Ahmed, A. and Hadid, A. and Benlamoudi, A.},
Booktitle = {Proceeding of the first International Conference on Electrical Engineering ICEEB’14},
Year = {2014},
DOI = {10.13140/RG.2.1.1933.6483/1}


Paper: PDF
Databases: FG-NET and PAL