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Tracking human face(s), Different background, Video sequences, FAST features, Corner points, Low resolution
We are proposing a novel algorithm for tracking human face(s) in different background video sequences. We have trained both face and non-face images which help in face(s) detection process. At first, FAST features and corner points are extracted from the detected face(s). Further, mid points are calculated from corner points. FAST features, corner points and mid points are combined together. Using the combined points, point tracker tracks face(s) in the frames of the video sequence. Standard metrics were adopted for measuring the performance of the proposed algorithm. Low resolution video sequences with challenges such as partial occlusion, changes in expression, variations in illumination and pose took part while testing the proposed algorithm. Test results clearly indicate the robustness of the proposed algorithm on all different background challenging video sequences.
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