Neural Architecture for Temporal Emotion Classification

In this pilot study, a neural architecture for temporal emotion recognition from image sequences is proposed. The investigation aims at the development of key principles in an extendable experimental framework to study human emotions. Features representing temporal facial variations were extracted within a bounding box around the face that is segregated into regions. Within each region, the optical flow is tracked over time. The dense flow field in a region is subsequently integrated whose principal components were estimated as a representative velocity of face motion. For each emotion a Fuzzy ARTMAP neural network was trained by incremental learning to classify the feature vectors resulting from the motion processing stage. Single category nodes corresponding to the expected feature representation code the respective emotion classes. The architecture was tested on the Cohn-Kanade facial expression database.