Online Multiple Face Detection and Tracking from Video

ABSTRACT: Online face detection and tracking systems have received increasing interest in the last decade. Face detection and tracking are subfield of biometric information processing and object tracking, respectively. Recent advances in theory and practical implementations made the online detection and tracking systems work in real time. Face detection and tracking system designed and implemented in this thesis exploits a combination of techniques in two topics; face detection and tracking. Face detection is performed on live achieved images from video. Processes exploited in the system are color balance, skin segmentation, and facial image extraction on face candidates. Then a face classification method that uses a Haar classifier is employed in the system. Finally, the result from detection part is engaged with a Kalman filter for tracking candidate faces in reasonable speed of change. The system is tested in practice and has shown to have acceptable performance for tracking faces within the proposed limits. The developed system also gave satisfactory results for multiple faces in live achieved images within each video frame. Keywords: Face detection, object tracking, facial feature extraction, Haar classifier, Kalman filter. …………………………………………………………………………………………………………………………