Human-Explainable Features for Job Candidate Screening Prediction

Achmadnoer Sukma Wicaksana, Cynthia C. S. Liem; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2017, pp. 14-19


Video blogs (vlogs) are a popular media form for people to present themselves. In case a vlogger would be a job candidate, vlog content can be useful for automatically assessing the candidate's traits, as well as potential interviewability. Using a dataset from the CVPR ChaLearn competition, we build a model predicting Big Five personality trait scores and interviewability of vloggers, explicitly targeting explainability of the system output to humans without technical background. We use human-explainable features as input, and a linear model for the system's building blocks. Four multimodal feature representations are constructed to capture facial expression, movement, and linguistic usage. For each, PCA is used for dimensionality reduction and simple linear regression for the predictive model. Our system's accuracy lies in the middle of the quantitative competition chart, while we can trace back the reasoning behind each score and generate a qualitative analysis report per video.

Related Material

author = {Sukma Wicaksana, Achmadnoer and Liem, Cynthia C. S.},
title = {Human-Explainable Features for Job Candidate Screening Prediction},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {July},
year = {2017}