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Choosing the right person for the right job makes the personnel interviewprocess a cognitively demanding task. Psychometric tests, followed by aninterview, have often been used to aid the process although such mechanismshave their limitations. While psychometric tests suffer from faking or socialdesirability of responses, the interview process depends on the way theresponses are analyzed by the interviewers. We propose the use of behaviometryas an assistive tool to facilitate an objective assessment of the intervieweewithout increasing the cognitive load of the interviewer. Behaviometry is arelatively little explored field of study in the selection process, thatutilizes inimitable behavioral characteristics like facial expressions,vocalization patterns, pupillary reactions, proximal behavior, body language,etc. The method analyzes thin slices of behavior and provides unbiasedinformation about the interviewee. The current study proposes the methodologybehind this tool to capture facial expressions, in terms of facial asymmetryand micro-expressions. Hemi-facial composites using a structural similarityindex was used to develop a progressive time graph of facial asymmetry, as atest case. A frame-by-frame analysis was performed on three YouTube videosamples, where Structural similarity index (SSID) scores of 75% and more showedbehavioral congruence. The research utilizes open-source computer visionalgorithms and libraries (python-opencv and dlib) to formulate the procedurefor analysis of the facial asymmetry.

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