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  Published Paper Details:

  Paper Title

TENSORFLOW-BASED AUTOMATIC PERSONALITY RECOGNITION USED IN ASYNCHRONOUS VIDEO INTERVIEWS

  Authors

  Kunal Raj,  Rakshit Raushan,  Harjasman Singh,  Digambar Patil

  Keywords

Affective computing, Big five, Resume analysis, Deep learning, Convolutional neural network (CNN) Lens model, Personality computing, TensorFlow

  Abstract


Today, Artificial Intelligence (AI) has reduced many problems and in fact it has made our lives very easy. Concerning about the project,we have used AI to avoid the method of recognizing the personality that one looks for to hire an employ in a particular company and becomes an asset for the company.It will take place with the help of CNN models which can successfully recognize human non-verbal cues like the posture and attribute their personality traits with the help of a camera. With the assistance of AI TensorFlow engine, Automatic character acknowledgment has been performed dependent on the highlights separated from the Asynchronous Video Interview (AVI) preparing and the genuine character scores from the outward appearances and self revealed polls of some genuine job candidates. The AI-based meeting specialist can enhance or supplant existing self-revealed character appraisal techniques that work candidates may mutilate to accomplish socially alluring impacts.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT2103694

  Paper ID - 205244

  Page Number(s) - 5882-5885

  Pubished in - Volume 9 | Issue 3 | March 2021

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Kunal Raj,  Rakshit Raushan,  Harjasman Singh,  Digambar Patil,   "TENSORFLOW-BASED AUTOMATIC PERSONALITY RECOGNITION USED IN ASYNCHRONOUS VIDEO INTERVIEWS", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.9, Issue 3, pp.5882-5885, March 2021, Available at :http://www.ijcrt.org/papers/IJCRT2103694.pdf

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ISSN: 2320-2882
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ISSN and 7.97 Impact Factor Details


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ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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