Head pose estimation using Fisher Manifold learning

• Published in: 2003 IEEE International SOI Conference. Proceedings (Cat. No.03CH37443) pp. 203 - 207
• Main Authors: Chen, L., Zhang, L., Hu, Y., Li, M., Zhang, H.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2003
• Subjects: Conferences
• ISBN: 0769520103; 9780769520100
• DOI: 10.1109/AMFG.2003.1240844

Here, we propose a new learning strategy for head pose estimation. Our approach uses nonlinear interpolation to estimate the head pose using the learning result from face images of two head poses. Advantage of our method to regression method is that it only requires training images of two head poses and better generalization ability. It outperforms existed methods, such as regression and multiclass classification method, on both synthesis and real face images. Average head pose estimation error of yaw rotation is about 4/sup 0/, which proves that our method is effective in head pose estimation.