Electromechnical Engineering College; Xinxiang University,Henan, 451003, China

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Abstract—A multiple faces tracking system was presented based on Relevance Vector Machine (RVM) and Boosting learning. At the first frame, a face detector based on AdaBoost is used to detect faces, and the face motion models and face color models are created. The face motion model consists of a set of RVMs that learn the relationship between the motion of the face and its appearance in the image, and the face color model is the 2D histogram of the face region in CrCb color space. In the tracking process, different tracking methods are used according to different states of the faces and the states are changed according to the tracking results. When the full image search condition is satisfied, a full image search is started in order to find new coming faces and former occluded faces.

Index Terms—Multiple faces tracking, relevance vector machine

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Cite: Wenxing Li and Liming Lian, "Multiple Faces Tracking Based on Relevance Vector Machine," Journal of Software vol. 7, no. 4, pp. 810-813, 2012.