Neural network model for multimodal recognition of human aggression

Growing user base of socio-cyberphysical systems, smart environments, IoT (Internet of Things) systems actualizes the problem of revealing of destructive user actions, such as various acts of aggression. Thereby destructive user actions can be represented in different modalities: locomotion, facial expression, associated with it, non-verbal speech behavior, verbal speech behavior. This paper considers a neural network model of multi-modal recognition of human aggression, based on the establishment of an intermediate feature space, invariant to the actual modality, being processed. The proposed model ensures high-fidelity aggression recognition in the cases when data on certain modality are scarce or lacking. Experimental research showed 81.8