Intelligent System for Bi-Modal Recognition of Apparent Personality Traits (iSMART)

Personality of an individual has been a promising variable to understand himself and furthermore the others in the society. It is the logical arrangement of an individual's attributes like thoughts, feelings, attitudes, behaviour and capability that makes an individual selective. Our personality likewise influences our decisions, medical conditions, assumptions, inclinations and prerequisites. In the scenario of 4G/5G and COVID pandemic, the majority of individuals are dependent on the web gateways as their essential intuitive vehicle for their own and expert necessities; accordingly, it has been a fundamental significance for us to consequently perceive the personality traits of the individual on the opposite side of the screen. Mental analysts have tracked down that an interaction of just 100 ms is adequate to shape judgement about any individual. Thinking about a similar idea towards execution of profound learning for recognition of personality traits, in this work, we propose an intelligent model (iSMART), a combination of depth-wise separable convolution neural network (2D-CNN) and long short-term memory with attention (LSTMwA), that extracts audio and video features through parallel networks and predicts the ultimate personality score of a person. With the top to bottom trial and error, it has been seen that the depth-wise separable CNN reduces the quantity of trainable parameters without compromising the test precision. It is a compelling and lightweight model for recognition of personality traits utilising bi-modular data sources. It likewise accomplishes better accuracy as compared with the outcomes got by the top scoring teams in the ChaLearn Looking at People challenge ECCV 2016. Our proposed model can possibly empower the system with better psychological understandings and improved human-computer interaction.