One popular solution to head pose estimation is to formulate it as a pattern classication problem, and
treat the holistic facial appearance as the input to classiers. However, since the face appearance contains
all kinds of information, the variation caused by other factors such as identity, expression and lighting
may be larger than that caused by dierent head poses. Thus, the key challenge of these appearance based
methods lies in constructing a feature subspace that could successfully recovers head pose while ignoring
other sources of image variation. In this paper, following the intuition of combining parts to form a
whole face, non-negative matrix factorization (NMF) is extended to learn a localized non-overlapping
subspace representation for head pose estimation. To emphasize the appearance variation in head poses,
one individual extended NMF subspace is learned for each pose. The head pose of a given face image
is then estimated based on its reconstruction error after being projected into the learned pose subspaces.
Experiments based on benchmark face database demonstrate the eciency of the proposed method.