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 that 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.

Khalifa, I., Ejbali, R., Zaied, M. (2019). Body gesture modeling for psychology analysis in job interview based on deep Spatio-temporal approach. In Parallel and Distributed Computing, Applications and Technologies (pp.274-284). Springer Verlag [10.1007/978-981-13-5907-1_29].

Body gesture modeling for psychology analysis in job interview based on deep Spatio-temporal approach

Khalifa, I;
2019

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 that 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.
paper
non-verbal behavior; body gestures; deep learning; EBMI; SAE
English
In Parallel and Distributed Computing, Applications and Technologies. PDCAT 2018
2018
Jong Hyuk Park; Hong ShenYunsick Sung; Hui Tian
Parallel and Distributed Computing, Applications and Technologies
9789811359064
2019
931
274
284
none
Khalifa, I., Ejbali, R., Zaied, M. (2019). Body gesture modeling for psychology analysis in job interview based on deep Spatio-temporal approach. In Parallel and Distributed Computing, Applications and Technologies (pp.274-284). Springer Verlag [10.1007/978-981-13-5907-1_29].
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/284934
Citazioni
  • Scopus 4
  • ???jsp.display-item.citation.isi??? ND
Social impact