HOG-Based Decision Tree for Facial Expression Classification

We address the problem of human emotion identification from still pictures taken in semi-controlled environments. Histogram of Oriented Gradient (HOG) descriptors are considered to describe the local appearance and shape of the face. First, we propose a Bayesian formulation to compute class specific edge distribution and log-likelihood maps over the entire aligned training set. A hierarchical decision tree is then built using a bottom-up strategy by recursively clustering and merging the classes at each level. For each branch of the tree we build a list of potentially discriminative HOG features using the log-likelihood maps to favor locations that we expect to be more discriminative. Finally, a Support Vector Machine (SVM) is considered for the decision process in each branch. The evaluation of the present method has been carried out on the Cohn-Kanade AU-Coded Facial Expression Database, recognizing different emotional states from single picture of people not present in the training set.