A Feature-Based Adaptive Model for Realtime Face Tracking on Smart Phones

Abstract

We propose a face tracking method that is extremely fast and applicable to implementation on smart phones. An adaptive model is built to make the tracking robust with variety of situation such as scaling, pose changes, and abrupt movements. For dealing with the scaling and appearance changes, we incorporate the Lukas and Kanade’s optical flow and the CAMShift in which both color and corner point features are used to achieve the high accuracy. Based on the feature tracking, the model adapts with abrupt movements of tracked face and mobile camera by a failure detection method. The system then utilizes the particle filter and CAMShift to catch up the fast motion. Based on the tracked region, we detect the face in order to reinitialize the tracking process. The proposed method shows the high performance against the recent ones as well as achieving the realtime requirement on smart phones.