A swarm-intelligence based algorithm for face tracking
by Yuhua Zheng, Yan Meng
International Journal of Intelligent Systems Technologies and Applications (IJISTA), Vol. 7, No. 3, 2009

Abstract: This article presents a new face tracking algorithm that employs a swarm-intelligence based method particle swarm optimisation (PSO). Firstly, all potential solutions are projected into a high-dimensional space where particles are initialised. Then, particles are driven by PSO rules to search for the solutions. The face is tracked when the particles reach convergence. Furthermore, a multi-feature model is also proposed for face description to enhance the tracking accuracy and efficiency. The proposed model and algorithm are object-independent and can be used for any free-selected object tracking. Experimental results on face tracking demonstrate that the proposed algorithm is efficient and robust in visual object tracking under dynamic environments with real-time performance.

Online publication date: Wed, 15-Jul-2009

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Intelligent Systems Technologies and Applications (IJISTA):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com