Automatic Personality Recognition In Interviews Using Convolution Neural Network (CNN)

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Ramya k, Sheba Selvam


Since the development of artificial intelligence, human-computer interaction, persona computing, and psychological testing have all benefited from the automated character assessment of video conversations to find personality traits. Deep learning (DL) techniques have enabled advancements in system cognition and creative and prescient abilities. Researchers have been able to recognise nonverbal cues and personality traits are attributes to them thanks to the creation of CNN (convolutional neural network) models capable of recreating human looks reliably via means of a camera. An entire AI system was used in this investigation. The video interviewing system and asynchronous video interview (AVI) processing were used to create the interviewing tool. Based only on the collected attributes, the Tensor Flow AI engine will robotically create personas (APR). Genuine personality ratings from facial expressions and self-reported surveys have been computed using the AVIs and real personality rankings from the AVIs. The studies' findings demonstrate that our AI-based interview bot is capable of accurately identifying an interviewee's "personality" traits. Our research also demonstrates that the semi-supervised deep learning technique surprise good performance in terms of despite the absence, automatic personality recognition of time-consuming manual labelling and annotating. This was true even in the absence of large-scale data. The Intelligence interview agent can be deployed in addition to or in substitute of current self-reported personality evaluation techniques, which job hopefuls can also manipulate to produce socially acceptable outcomes.

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