Robust Head Pose Estimation Using Textured Polygonal Model with Local Correlation Measure

In this paper, a robust head pose estimation algorithm is presented. In contrast with other approaches, the proposed algorithm adopts textured polygonal model generated from two orthogonal views for accurate head pose estimation. To achieve robust estimation under varying illumination, local correlation coefficient is taken as the similarity measure. The tracking is further improved by modeling head dynamics with Kalman filtering. Preliminary simulation results indicate that the proposed algorithm can reliably estimate the head pose under large rotation angles with varying illumination, and the average estimation error are all below 4 degrees.

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