An NARX-based approach for human emotion identification

This paper presents a Nonlinear AutoRegressive with eXogenous input (NARX)-based approach for human-emotion recognition from an input video. The dynamics of facial expressions are first captured by performing a temporal-spatial analysis by extracting local and spatial features using a pyramid of histograms of oriented gradients (PHOG) descriptor. Then the temporal phases of facial expressions are identified using our proposed Mutual-Information-based Delay Identification Algorithm. Finally, the emotion recognition problem is formulated into a parametric regression context using a recurrent NARX network. This approach enhances the cognitive skills of humanoid robots by adding the ability to recognize and understand affective emotional states of a human. Computer simulations were conducted to illustrate the performance of the proposed NARX-based approach with recurrent neural network realization. The proposed recurrent NARX network performed better than other existing human-emotion recognition systems and achieved a 91.5% recognition rate when tested using the Cohn-Kanade database.