Personality Traits and Job Candidate Screening via Analyzing Facial Videos

Salah Eddine Bekhouche, Fadi Dornaika, Abdelkrim Ouafi, Abdelmalik Taleb-Ahmed; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2017, pp. 10-13

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.