Constrained Particle Swarm Optimization

In this Chapter, we present a new face detection and tracking algorithm using Bayesconstrained particle swarm optimization (BC-PSO), which is a population based searching algorithm. A cascade of boosted classifiers based on Haar-like features is trained and employed for object detection. Then the PSO-based algorithm is applied for object tracking. Basically the searching can be divided into two steps in this method. First, the object model is projected into a high-dimensional feature space, and a PSO algorithm is applied to search over this high-dimensional space and converge to some global optima, which are well-matched candidates in terms of object features. Second, a Bayes-based filter is used to identify the one with the highest possibility among these candidates under the constraint of object motion estimation. The proposed algorithm considers not only the object features but also the object motion estimation to speed up the searching procedure. Experimental results demonstrate that the proposed method is efficient and robust under dynamic environment.