Facial age estimation and gender classification using multi level local phase quantization

Bekhouche International conference papers
Facial age estimation and gender classification using multi level local phase quantization

Example of ML-LPQ features extraction with 4 level

Authors:

Salah Eddine Bekhouche1, Abdelkrim Ouafi1,
Azeddine Benlamoudi2, Abdelmalik Taleb-Ahmed3 and Abdenour Hadid4

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

Abstract:

Facial demographic classification is an attractive topic in computer vision. Attributes such as age and gender can be used in many real life application such as face recognition and internet safety for minors. In this paper, we present a novel approach for age estimation and gender classification under uncontrolled conditions following the standard protocols for fair comparaison. Our proposed approach is based on Multi Level Local Phase Quantization (ML-LPQ) features which are extracted from normalized face images. Two different Support Vector Machines (SVM) models are used to predict the age group and the gender of a person. The experimental results on the benchmark Image of Groups dataset showed the superiority of our approach compared to the state-of-the-art.

Please cite as:

@InProceedings{CEIT2015AGE,
author={S. E. Bekhouche and A. Ouafi and A. Benlamoudi and A. Taleb-Ahmed and A. Hadid},
booktitle={2015 3rd International Conference on Control, Engineering Information Technology (CEIT)},
title={Facial age estimation and gender classification using multi level local phase quantization},
year={2015},
pages={1-4},
doi={10.1109/CEIT.2015.7233141},
month={May}
}

Downloads:

Paper: PDF
Database: IoG
Code: Matlab (Multi-Level features)
Data: Images of Groups (IoG)