Prompt Assessment of Facial Expressions to Foresee Behavioral Aptitudes in Video Interviews

This endeavor seeks to engineer a sophisticated real-time image and video processor, augmented with an advanced artificial intelligence (AI) agent, capable of prognosticating a candidate’s behavioral competencies through the nuanced analysis of their facial expressions. This achievement leverages a real-time, video-recorded interview utilizing a histogram of oriented gradients and support vector machine (HOG-SVM) in conjunction with convolutional neural network (CNN) recognition. Diverging from the traditional paradigm of emotional state recognition, this avant-garde prototype system is designed to autonomously decipher a job candidate’s behaviors through their micro expressions, grounded in the behavioral ecology view of facial displays (BECV), within the framework of employment interviews. This manuscript introduces a pioneering methodology for evaluating a candidate’s performance in a video interview. It elucidates a comprehensive study of sentiment analysis and eye-tracking techniques, whereby results can be synthesized on a single display to facilitate the selection of the most suitable candidate for hire. An empirical study was executed at a enterprise, wherein video recordings and competency evaluations were gathered from the organization’s staff and hiring executives. The findings revealed that our proposed system exhibits superior predictive capabilities compared to traditional human-structured interviews, personality assessments, occupational interest evaluations, and assessment center. Consequently, our innovative methodology can be effectively employed as a screening tool, leveraging a personal-value-based competency framework.