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Body Gesture Modeling for Psychology Analysis in Job Interview Based on Deep Spatio-Temporal Approach

  • Intissar KhalifaEmail author
  • Ridha Ejbali
  • Mourad Zaied
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 931)

Abstract

Social psychologists have long studied job interviews with the aim of knowing the relationships between behaviors, interview outcomes, and job performance. Several companies give great importance to psycho-test based on observation of the candidate is behavior more than the answers they even especially in sensitive positions like trade, marketing, investigation, etc. Our work will be a combination between two interesting topics of research in the last decades which are social psychology and affective computing. Some techniques were proposed until today to analyze automatically the candidate is non verbal behavior. This paper concentrates in body gestures which is an important non-verbal expression channel during affective communication that is not very studied in comparison to facial expressions. We proposed in this work a deep Spatio-temporal approach, it merges the temporal normalization method which is the energy binary motion information (EBMI) with deep learning based on stacked auto-encoder (SAE) for emotional body gesture recognition in job interview and the results prove the efficiency of our proposed approach.

Keywords

Non-verbal behavior Body gestures Deep learning EBMI SAE 

Notes

Acknowledgment

The authors would like to acknowledge the financial support of this work by grants from General Direction of Scientific Research (DGRST), Tunisia, under the ARUB program.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Research Team in Intelligent MachinesNational Engineering School of GabesGabesTunisia

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