Assessing Conscientiousness and Identify Leadership Quality Using Temporal Sequence Images

作     者：T.S.Kanchana B.Smitha Evelin Zoraida 

作者机构：Department of Computer ScienceBharathidasan UniversityKhaja Mali CampusTiruchirappalliIndia 

出 版 物：《Intelligent Automation & Soft Computing》 (智能自动化与软计算（英文）)

年 卷 期：2023年第35卷第2期

页      面：2003-2013页

核心收录：

学科分类：08[工学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Bigfive ocean deep learning set personality traits 

摘      要：Human Facial expressions exhibits the inner *** the inner personality is performed through questionnaires during recruitment ***,the evaluation through questionnaires performs less due to anxiety,and stress during interview and prediction of leadership quality becomes a challenging *** the above problem,Temporal sequence based SENet architecture(TSSA)is proposed for accurate evaluation of personality trait for employing the correct person for leadership ***,SENet is integration with modern architectures for performance *** 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 *** a days,Facebook plays a vital role,where people express their emotions by posting images and updating their profile *** 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 personal-ity *** 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 *** images for predicting the personality,where asked tofill the Questionnaires through Google Forms increase the accuracy in *** TSSA prediction results are utilized for assessment of a person’s conscientiousness for leadership quality *** study implements Deep Learning algorithm with SENet architecture and compares with traditional *** the validation results the proposed TSSA method performs 96%of accuracy in conscientiousness prediction.