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Although automatic emotion recognition from facial expressions and speech hasmade remarkable progress, emotion recognition from body gestures has not beenthoroughly explored. People often use a variety of body language to expressemotions, and it is difficult to enumerate all emotional body gestures andcollect enough samples for each category. Therefore, recognizing new emotionalbody gestures is critical for better understanding human emotions. However, theexisting methods fail to accurately determine which emotional state a new bodygesture belongs to. In order to solve this problem, we introduce a GeneralizedZero-Shot Learning (GZSL) framework, which consists of three branches to inferthe emotional state of the new body gestures with only their semanticdescriptions. The first branch is a Prototype-Based Detector (PBD) which isused to determine whether an sample belongs to a seen body gesture category andobtain the prediction results of the samples from the seen categories. Thesecond branch is a Stacked AutoEncoder (StAE) with manifold regularization,which utilizes semantic representations to predict samples from unseencategories. Note that both of the above branches are for body gesturerecognition. We further add an emotion classifier with a softmax layer as thethird branch in order to better learn the feature representations for thisemotion classification task. The input features for these three branches arelearned by a shared feature extraction network, i.e., a Bidirectional LongShort-Term Memory Networks (BLSTM) with a self-attention module. We treat thesethree branches as subtasks and use multi-task learning strategies for jointtraining. The performance of our framework on an emotion recognition dataset issignificantly superior to the traditional method of emotion classification andstate-of-the-art zero-shot learning methods.

A Generalized Zero-Shot Framework for Emotion Recognition from Body Gestures