  Facial expression is the most natural means for human beings to communicate
their emotions. Most facial expression analysis studies consider the case of
acted expressions. Spontaneous facial expression recognition is significantly
more challenging since each person has a different way to react to a given
emotion. We consider the problem of recognizing spontaneous facial expression
by learning discriminative dictionaries for sparse representation. Facial
images are represented as a sparse linear combination of prototype atoms via
Orthogonal Matching Pursuit algorithm. Sparse codes are then used to train an
SVM classifier dedicated to the recognition task. The dictionary that
sparsifies the facial images (feature points with the same class labels should
have similar sparse codes) is crucial for robust classification. Learning
sparsifying dictionaries heavily relies on the initialization process of the
dictionary. To improve the performance of dictionaries, a random face feature
descriptor based on the Random Projection concept is developed. The
effectiveness of the proposed method is evaluated through several experiments
on the spontaneous facial expressions DynEmo database. It is also estimated on
the well-known acted facial expressions JAFFE database for a purpose of
comparison with state-of-the-art methods.
