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The advancement of Human-Robot Interaction (HRI) drives research into the development of advanced emotion identification architectures that fathom audio-visual (A-V) modalities of human emotion. State-of-the-art methods in multi-modal emotion recognition mainly focus on the classification of complete video sequences, leading to systems with no online potentialities. Such techniques are capable of predicting emotions only when the videos are concluded, thus restricting their applicability in practical scenarios. This article provides a novel paradigm for online emotion classification, which exploits both audio and visual modalities and produces a responsive prediction when the system is confident enough. We propose two deep Convolutional Neural Network (CNN) models for extracting emotion features, one for each modality, and a Deep Neural Network (DNN) for their fusion. In order to conceive the temporal …