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Conference Papers Year : 2012

Multi-constraints Face Detect-Track System


This paper presents a new system to achieve face detection and tracking in video sequences. We have performed a combination between detection and tracking modules to overcome the different challenging problems that can occur while detecting or tracking faces. Our proposed system is composed of two modules: Face detection module and face tracking module. In the face detection module, we have used skin color and motion information to extract regions of interest and cut off false positive face. This filtering step has enhanced the next face tracking processing step, as it helps to avoid tracking false positive faces. Regarding tracking module, we have used face detection results to keep the face tracker updated. In order to carry on tracking face we have used particle filter technique which was adapted to track multiple faces. Moreover, each tracked face was described by a defined state: tracked, occluded, entered, left or stopped. The performance of our detect-track system was evaluated using several experiments. This evaluation proved the robustness of our face detection-track system as it supports automatic tracking with no need to manual initialization or re-initialization and reaches best performance to deal with different challenging problems.
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hal-01551727 , version 1 (30-06-2017)





Hazar Mliki, Mohamed Ali Hammami, Hanêne Ben-Abdallah. Multi-constraints Face Detect-Track System. 11th International Conference on Computer Information Systems and Industrial Management (CISIM), Sep 2012, Venice, Italy. pp.224-235, ⟨10.1007/978-3-642-33260-9_19⟩. ⟨hal-01551727⟩
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