Face tracking using mean-shift algorithm: a fuzzy approach for boundary detection

Authors:
Farhad Dadgostar;Abdolhossein Sarrafzadeh;Scott P. Overmyer
Affiliations:
Institute of Information and Mathematical Sciences, Massey University, Auckland, New Zealand;Institute of Information and Mathematical Sciences, Massey University, Auckland, New Zealand;Department of Electrical Engineering & Computer Science, South Dakota State University, Brookings, SD
Venue:
ACII'05 Proceedings of the First international conference on Affective Computing and Intelligent Interaction
Year:
2005

Citing 3
Cited 1

Mean Shift, Mode Seeking, and Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking Discontinuous Motion Using Bayesian Inference

ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
A real-time head nod and shake detector

Proceedings of the 2001 workshop on Perceptive user interfaces

Multi-layered hand and face tracking for real-time gesture recognition

ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

Face and hand tracking are important areas of research, related to adaptive human-computer interfaces, and affective computing. In this article we have introduced two new methods for boundary detection of the human face in video sequences: (1) edge density thresholding, and (2) fuzzy edge density. We have analyzed these algorithms based on two main factors: convergence speed and stability against white noise. The results show that “fuzzy edge density” method has an acceptable convergence speed and significant robustness against noise. Based on the results we believe that this method of boundary detection together with the mean-shift and its variants like cam-shift algorithm, can achieve fast and robust tracking of the face in noisy environment, that makes it a good candidate for use with cheap cameras and real-world applications.