Head pose estimation based on extended non-negative matrix factorization

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.