Recognition of Asymmetric Facial Action Unit Activities and Intensities
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Real time facial expression recognition in video using support vector machines
Proceedings of the 5th international conference on Multimodal interfaces
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognising facial expressions in video sequences
Pattern Analysis & Applications
Fast Human Pose Detection Using Randomized Hierarchical Cascades of Rejectors
International Journal of Computer Vision
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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.