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

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

Emotions and Their Intensity in Hindustani Classical Music Using Two Rating Interfaces

One of the very popular techniques of assessing music is using the dimensional model. Although it is used in numerous studies, the discrete model is of great importance in the Indian tradition. This study assesses two discrete interfaces for continuous rating of Hindustani classical music. The first interface, the Discrete emotion wheel (DEW), captures the range of eight aesthetic emotions relevant to Hindustani classical music and cited in Natyashastra. The second interface, the Intensity-rating emotion wheel (IEW), assesses the emotional arousal and identifies whether the additional cognitive load interferes with accurate rating. Forty-eight participants rated emotions expressed by five Western and six Hindustani classical clips. Results suggest that both the interfaces work effectively for both the music genres. The intensity-rating emotion wheel was able to capture arousal in the clips where they show higher intensities in the dominant emotions. Implications of the tool for assessing the relation between musical structures, emotions, and time are also discussed