A Lifelong Learning Approach for Improving Accurate Face Recognition

DOI

10.2991/icaita-16.2016.2How to use a DOI?

Keywords

face recognition; meachine learning; neural network; lifelong learning; artificial intelligence

Abstract

With the popularity of artificial intelligence and computer vision, an increasing number of software engineers attempt to make their systems be able to recognize the user, through the way of face recognition, e.g., Characteristic Points (i.e., CP)-based face recognition. Generally, the traditional CP-based face recognition only work when the user’s face is not much different from the one that stores in the system. However, users’ faces can change a lot intentionally or unintentionally, which brings a great challenge for correct face recognition. In view of this challenge, a novel face recognition approach LL-FR (Lifelong Learning-based Face Recognition) is put forward in this paper. Concretely, the system stores not only the face image that the user registered, but also the images every time when it recognizes the user’s face. Afterwards, the system makes the future recognition of the user based on all the previous faces of him/her. Finally, through a set of experiments, we demonstrate the feasibility of our approach.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Zhangqu Yu
PY  - 2016/01
DA  - 2016/01
TI  - A Lifelong Learning Approach for Improving Accurate Face Recognition
BT  - Proceedings of the 2016 International Conference on Artificial Intelligence: Technologies and Applications
PB  - Atlantis Press
SP  - 6
EP  - 8
SN  - 1951-6851
UR  - https://doi.org/10.2991/icaita-16.2016.2
DO  - 10.2991/icaita-16.2016.2
ID  - Yu2016/01
ER  -