Real time facial feature points tracking with Pyramidal Lucas-Kanade algorithm

In this paper, we present a detection and tracking feature points algorithm in real time camera input environment. To trace and extract a face image, we use a modified face detector based on the Haar-like features. For feature points detection, we use good features to track of Shi and Thomasi. In order to track the facial feature points, pyramidal Lucas-Kanade feature tracker algorithm is used. Results on the real time indicate that the proposed algorithm can accurately extract facial features points.

[1]  Frank Y. Shih,et al.  Automatic extraction of head and face boundaries and facial features , 2004, Inf. Sci..

[2]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[3]  Paul A. Viola,et al.  Robust Real-time Object Detection , 2001 .

[4]  Mu-Chun Su,et al.  A simple approach to facial expression recognition , 2007 .

[5]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[6]  J.-Y. Bouguet,et al.  Pyramidal implementation of the lucas kanade feature tracker , 1999 .

[7]  Hyung-Il Choi,et al.  Real Time Face Tracking with Pyramidal Lucas-Kanade Feature Tracker , 2007, ICCSA.

[8]  Rachid Belaroussi Face Tracking and Facial Feature Detection with a Webcam , 2006 .

[9]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[10]  Dimitris N. Metaxas,et al.  A Hierarchical Framework For High Resolution Facial Expression Tracking , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[11]  Zoltán Vámossy,et al.  PAL Based Localization Using Pyramidal Lucas-Kanade Feature Tracker , 2005 .

[12]  Alain Pruski,et al.  Gradient based method for static facial features localization , 2007 .