Efficient face detection and tracking with extended CAMSHIFT and haar-like features
Lae-Kyoung Lee, Su-Yong An, Se‐Young Oh · 2011
This paper presents a new approach to solve the problem of real-time robust face detection and tracking in complex environment. We propose a two level approach to detect faces and tracking. The lower level of the approach implements the extraction of face candidates using the combination of skin color model and haar-like features based adaboost learning algorithm. With this method, multiple-view faces are able to be detected in real-time with high recognition accuracy. The higher level of approach implements the robust face tracking with extended CAMSHIFT (Continuous Adaptive Mean SHIFT). The experimental results show that the proposed algorithm is robust and efficient to detect and track the face-of-interest in the cases of clutter background and the occurrence of occlusion.