Emotion recognition using dynamic grid-based HoG features

Automatic facial expression analysis is the most commonly studied aspect of behavior understanding and human-computer interface. The main difficulty with facial emotion recognition system is to implement general expression models. The same facial expression may vary differently across humans; this can be true even for the same person when the expression is displayed in different contexts. These factors present a significant challenge for the recognition task. The method we applied, which is reminiscent of the “baseline method”, utilizes dynamic dense appearance descriptors and statistical machine learning techniques. Histograms of oriented gradients (HoG) are used to extract the appearance features by accumulating the gradient magnitudes for a set of orientations in 1-D histograms defined over a size-adaptive dense grid, and Support Vector Machines with Radial Basis Function kernels are the base learners of emotions. The overall classification performance of the emotion detection reached 70% which is better than the 56% accuracy achieved by the “baseline method” presented by the challenge organizers.