Predicting behavioral competencies automatically from facial expressions in real-time video-recorded interviews

This work aims to develop a real-time image and video processor enabled with an artificial intelligence (AI) agent that can predict a job candidate's behavioral competencies according to his or her facial expressions. This is accomplished using a real-time video-recorded interview with a histogram of oriented gradients and support vector machine (HOG-SVM) plus convolutional neural network (CNN) recognition. Different from the classical view of recognizing emotional states, this prototype system was developed to automatically decode a job candidate's behaviors by their microexpressions based on the behavioral ecology view of facial displays (BECV) in the context of employment interviews using a real-time video-recorded interview. An experiment was conducted at a Fortune 500 company, and the video records and competency scores were collected from the company's employees and hiring managers. The results indicated that our proposed system can provide better predictive power than can human-structured interviews, personality inventories, occupation interest testing, and assessment centers. As such, our proposed approach can be utilized as an effective screening method using a personal-value-based competency model.

Automated summary

The study developed a real-time image and video processor with an AI agent to predict a job candidate's behavioral competencies based on facial expressions, showing better predictive power than traditional interviews in experiments at a Fortune 500 company.