Affect recognition from facial movements and body gestures by hierarchical deep spatio-temporal features and fusion strategy

Affect presentation is periodic and multi-modal, such as through facial movements, body gestures, and so on. Studies have shown that temporal selection and multi-modal combinations may benefit affect recognition. In this article, we therefore propose a spatio-temporal fusion model that extracts spatio-temporal hierarchical features based on select expressive components. In addition, a multi-modal hierarchical fusion strategy is presented. Our model learns the spatio-temporal hierarchical features from videos by a proposed deep network, which combines a convolutional neural networks (CNN), bilateral long short-term memory recurrent neural networks (BLSTM-RNN) with principal component analysis (PCA). Our approach handles each video as a “video sentence.” It first obtains a skeleton with the temporal selection process and then segments key words with a certain sliding window. Finally, it obtains the features with a deep network comprised of a video-skeleton and video-words. Our model combines the feature level and decision level fusion for fusing the multi-modal information. Experimental results showed that our model improved the multi-modal affect recognition accuracy rate from 95.13% in existing literature to 99.57% on a face and body (FABO) database, our results have been increased by 4.44%, and it obtained a macro average accuracy (MAA) up to 99.71%.