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Assessing Conscientiousness and Identify Leadership Quality Using Temporal Sequence Images

T. S. Kanchana*, B. Smitha Evelin Zoraida

Department of Computer Science, Bharathidasan University, Khaja Mali Campus, Tiruchirappalli, India

* Corresponding Author: T. S. Kanchana. Email: email

Intelligent Automation & Soft Computing 2023, 35(2), 2003-2013. https://doi.org/10.32604/iasc.2023.029412

Abstract

Human Facial expressions exhibits the inner personality. Evaluating the inner personality is performed through questionnaires during recruitment process. However, the evaluation through questionnaires performs less due to anxiety, and stress during interview and prediction of leadership quality becomes a challenging problem. To the above problem, Temporal sequence based SENet architecture (TSSA) is proposed for accurate evaluation of personality trait for employing the correct person for leadership position. Moreover, SENet is integration with modern architectures for performance evaluation. In Proposed TSSA, face book facial images of a particular person for a period of one month and face images collect from different social environments and forms the sequential facial image database are analysed for personality trait estimation. Now a days, Facebook plays a vital role, where people express their emotions by posting images and updating their profile pictures. In TSSA method, 50 Facebook temporal sequence of images of person with answered questionaries during the face image collection forms as a Temporal sequence image (TSI) database for prediction of the Big Five personality trait. In order to get precise prediction, we have analysed the face images that were posted in a period of one month and validated the result with the next month face images from face book. Face images for predicting the personality, where asked to fill the Questionnaires through Google Forms increase the accuracy in prediction. The TSSA prediction results are utilized for assessment of a person’s conscientiousness for leadership quality suitability. The study implements Deep Learning algorithm with SENet architecture and compares with traditional algorithms. From the validation results the proposed TSSA method performs 96% of accuracy in conscientiousness prediction.

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Cite This Article

T. S. Kanchana and B. S. E. Zoraida, "Assessing conscientiousness and identify leadership quality using temporal sequence images," Intelligent Automation & Soft Computing, vol. 35, no.2, pp. 2003–2013, 2023.



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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