Personality Traits and Job Candidate Screening via Analyzing Facial Videos - Université Polytechnique des Hauts-de-France Accéder directement au contenu
Communication Dans Un Congrès Année : 2017

Personality Traits and Job Candidate Screening via Analyzing Facial Videos

Salah Eddine Bekhouche
Fadi Dornaika
Abdelkrim Ouafi
  • Fonction : Auteur

Résumé

In this paper, we propose a novel approach for estimating the Big Five personality traits and the job candidate screening attribute through facial videos. At running time, the proposed system feeds the Pyramid Multi-Level (PML) texture features extracted from the whole video sequence to 5 Support Vector Regressors in order to estimate the personality traits. These estimated five scores are then considered as new input features to the interview score regressor. The latter is given by a Gaussian Process Regression (GPR). The experimental results on ChaLearn LAP APA2016 dataset achieve good performance. Furthermore, they demonstrate that the computational cost of both the training and the testing of the proposed framework are very competitive in terms of accuracy and computational cost. © 2017 IEEE.
Fichier non déposé

Dates et versions

hal-03665995 , version 1 (12-05-2022)

Identifiants

Citer

Salah Eddine Bekhouche, Fadi Dornaika, Abdelkrim Ouafi, Abdelmalik Taleb-Ahmed. Personality Traits and Job Candidate Screening via Analyzing Facial Videos. 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Jul 2017, Honolulu, HI, United States. pp.1660-1663, ⟨10.1109/CVPRW.2017.211⟩. ⟨hal-03665995⟩
6 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More