Facial asymmetry: A Computer Vision based behaviometric index for
  assessment during a face-to-face interview
Shuvam Keshari Tanusree Dutta Raju Mullick Ashish Rathor Priyadarshi Patnaik
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.