Evolving ensemble classifiers for incremental face recognition

Face recognition is an important research issue in many modern applications. Traditional face recognition technologies need collect the set of enough face images to learn an accurate face classifier for a specific person. However, collecting face images at a time is not easy for most people in real...

Full description

Saved in:
Bibliographic Details
Published in2012 International Conference on Machine Learning and Cybernetics Vol. 4; pp. 1559 - 1564
Main Authors Been-Chian Chien, Chiao-Ro Lin, Pe-Ting Hou, Rong-Ming Chen
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2012
Subjects
Abstracts
Ensemble classifier
Face recognition
Image recognition
Incremental learning
On-line learning
Random decision trees
Streaming media
Online AccessGet full text

Cover

More Information
Summary:Face recognition is an important research issue in many modern applications. Traditional face recognition technologies need collect the set of enough face images to learn an accurate face classifier for a specific person. However, collecting face images at a time is not easy for most people in real applications. In this paper, we propose an incremental learning method based on data selection and random decision trees to learn ensemble classifiers for on-line face recognition. The experimental results show that the proposed incremental learning method is efficient and highly adaptive. The learned classifiers also have accurate and stable recognition rate under a limited number of training face datasets.
ISBN:1467314846
9781467314848
ISSN:2160-133X
DOI:10.1109/ICMLC.2012.6359597