Cutting-Edge Methods in Visage Expression Recognition and Ocular Tracking for Analyzing Remote Video Interviews

This paper focuses to augment an in the flesh image and video processor, capacitated with an artificial intelligence (AI) arbitrator that can speculate a job interviewee’s behavioral adeptness according to the facial dictions or expressions. We put forward a sentiment analysis model using machine learning and CNN for the reinforcement of decision-making in the job interview process. This is bought up by histogram of oriented gradients and support vector machine (HOG-SVM) with the addition of convolutional neural network (CNN) recognition in real-time video recorded interviews. The goal of applying this technology, is to develop a method that could automatically decode a candidate’s behavior by his or her facial language (or micro expressions) based on the behavioral ecology view of facial displays (BECV) which is different from the classical perspective of recognizing emotional states. This paper dispenses a trailblazing technique of determining the performance of a candidate in a video interview. To do this, a delineation of the analysis of sentiments and eye tracking technique study is intricated in which the results can be processed on a single screen to select the right person for the hire.