Head pose estimation using Fisher Manifold learning
Chen, L.
Zhang, L.
Hu, Y.
Li, M.
Zhang, H.
Dept. of Electr. & Comput. Eng., Miami Univ., Coral Gables, FL, USA;
Abstract
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 40, which proves that our method is effective in head pose estimation.
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