IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Face Tracking Using Color
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Sequential Monte Carlo methods for multiple target tracking anddata fusion
IEEE Transactions on Signal Processing
Visual tracking and recognition using appearance-adaptive models in particle filters
IEEE Transactions on Image Processing
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In this paper, a multiple features face tracking algorithm based on particle filter is proposed. Particle filter can effectively combine multiple face features information which supply robustness in different environments. Meanwhile, our approach makes use of the invariance to rotation and translation of color histogram central moment and statistical characteristic of multiple resolution Sobel Local Binar'y Pattern (LBP) histogram which shows the local and enhanced global information, then fuses multiple features information by a weight proportion in particle filter framework to propose a new human face tracking algorithm. The experimental results demonstrate the efficiency and effectiveness of the algorithm and present a more robust face tracking performance compared with the method based on single feature.