Robust Face Track Finding in Video Using Tracked Points

Abstract

We present an robust method for detecting face tracks in video in which each face track represents one individual. Such face tracks are important for many potential applications such as video face recognition, face matching, and face-name association. The basic idea is to use the Kanade-Lucas-Tomasi (KLT) tracker to track interest points throughout video frames, and each face track is formed by the faces detected in different frames that share a large enough number of tracked points. However, since interest points are sensitive to illumination changes, occlusions, and false face detections, face tracks are often fragmented. Our proposed method maintains tracked points of faces instead of shots, and interest points are re-computed in every frame to avoid these issues. Experimental results on different long video sequences show the effectiveness of our approach.