Facial asymmetry: A Computer Vision based behaviometric index for assessment during a face-to-face interview

Abstract: Choosing the right person for the right job makes the personnel interview process a cognitively demanding task. Psychometric tests, followed by an interview, have often been used to aid the process although such mechanisms have their limitations. While psychometric tests suffer from faking or social desirability of responses, the interview process depends on the way the responses are analyzed by the interviewers. We propose the use of behaviometry as an assistive tool to facilitate an objective assessment of the interviewee without increasing the cognitive load of the interviewer. Behaviometry is a relatively little explored field of study in the selection process, that utilizes inimitable behavioral characteristics like facial expressions, vocalization patterns, pupillary reactions, proximal behavior, body language, etc. The method analyzes thin slices of behavior and provides unbiased information about the interviewee. The current study proposes the methodology behind this tool to capture facial expressions, in terms of facial asymmetry and micro-expressions. Hemi-facial composites using a structural similarity index was used to develop a progressive time graph of facial asymmetry, as a test case. A frame-by-frame analysis was performed on three YouTube video samples, where Structural similarity index (SSID) scores of 75% and more showed behavioral congruence. The research utilizes open-source computer vision algorithms and libraries (python-opencv and dlib) to formulate the procedure for analysis of the facial asymmetry.