In this paper we present the results of an experimental Italian research project finalized to support the classification process of the two behavioural status (resonance and dissonance) of a candidate applying for a job position. The proposed framework is based on an innovative system designed and implemented to extract and process the non-verbal expressions like facial, gestural and prosodic of the subject, acquired during the whole job interview session. In principle, we created our own database, containing multimedia data extracted, by different software modules, from video, audio and 3D sensor streams and then used SVM classifiers that perform in terms of accuracy 72%, 79% and 63% respectively for facial, vocal and gestural features. ANN classifiers have also been used, obtaining comparable results. Finally, we combined all the three domains and then reported the results of this last classification test proving that the experimental proposed work seems to perform in a very encouraging way. © 2014 Springer International Publishing Switzerland.

Evaluation of resonance in staff selection through multimedia contents / Bevilacqua, Vitoantonio; Salatino, Angelo Antonio; Di Leo, Carlo; D'Ambruoso, Dario; Suma, Marco; Barone, Donato; Tattoli, Giacomo; Campagna, Domenico; Stroppa, Fabio; Pantaleo, Michele (LECTURE NOTES IN COMPUTER SCIENCE). - In: Intelligent Computing Methodologies: 10th International Conference, ICIC 2014, Taiyuan, China, August 3-6, 2014. Proceedings / [a cura di] De-Shuang Huang; Kang-Hyun Jo; Ling Wang. - STAMPA. - Cham, CH : Springer, 2014. - ISBN 978-3-319-09338-3. - pp. 185-198 [10.1007/978-3-319-09339-0_19]

Evaluation of resonance in staff selection through multimedia contents

Bevilacqua, Vitoantonio;
2014-01-01

Abstract

In this paper we present the results of an experimental Italian research project finalized to support the classification process of the two behavioural status (resonance and dissonance) of a candidate applying for a job position. The proposed framework is based on an innovative system designed and implemented to extract and process the non-verbal expressions like facial, gestural and prosodic of the subject, acquired during the whole job interview session. In principle, we created our own database, containing multimedia data extracted, by different software modules, from video, audio and 3D sensor streams and then used SVM classifiers that perform in terms of accuracy 72%, 79% and 63% respectively for facial, vocal and gestural features. ANN classifiers have also been used, obtaining comparable results. Finally, we combined all the three domains and then reported the results of this last classification test proving that the experimental proposed work seems to perform in a very encouraging way. © 2014 Springer International Publishing Switzerland.
2014
Intelligent Computing Methodologies: 10th International Conference, ICIC 2014, Taiyuan, China, August 3-6, 2014. Proceedings
978-3-319-09338-3
Springer
Evaluation of resonance in staff selection through multimedia contents / Bevilacqua, Vitoantonio; Salatino, Angelo Antonio; Di Leo, Carlo; D'Ambruoso, Dario; Suma, Marco; Barone, Donato; Tattoli, Giacomo; Campagna, Domenico; Stroppa, Fabio; Pantaleo, Michele (LECTURE NOTES IN COMPUTER SCIENCE). - In: Intelligent Computing Methodologies: 10th International Conference, ICIC 2014, Taiyuan, China, August 3-6, 2014. Proceedings / [a cura di] De-Shuang Huang; Kang-Hyun Jo; Ling Wang. - STAMPA. - Cham, CH : Springer, 2014. - ISBN 978-3-319-09338-3. - pp. 185-198 [10.1007/978-3-319-09339-0_19]
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/11589/84240
Citazioni
  • Scopus 1
  • ???jsp.display-item.citation.isi??? 0
social impact