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Human emotion is expressed, perceived and captured using a variety of dynamicdata modalities, such as speech (verbal), videos (facial expressions) andmotion sensors (body gestures). We propose a generalized approach to emotionrecognition that can adapt across modalities by modeling dynamic data asstructured graphs. The motivation behind the graph approach is to build compactmodels without compromising on performance. To alleviate the problem of optimalgraph construction, we cast this as a joint graph learning and classificationtask. To this end, we present the Learnable Graph Inception Network (L-GrIN)that jointly learns to recognize emotion and to identify the underlying graphstructure in the dynamic data. Our architecture comprises multiple novelcomponents: a new graph convolution operation, a graph inception layer,learnable adjacency, and a learnable pooling function that yields a graph-levelembedding. We evaluate the proposed architecture on five benchmark emotionrecognition databases spanning three different modalities (video, audio, motioncapture), where each database captures one of the following emotional cues:facial expressions, speech and body gestures. We achieve state-of-the-artperformance on all five databases outperforming several competitive baselinesand relevant existing methods. Our graph architecture shows superiorperformance with significantly fewer parameters (compared to convolutional orrecurrent neural networks) promising its applicability to resource-constraineddevices.

Dynamic Emotion Modeling with Learnable Graphs and Graph Inception Network