Comparing CNN and RNN for Prediction of Judgement in Video Interview Based on Facial Gestures

This paper presents a novel technique of judging the performance of a candidate in a video interview. The candidate is judged as confident and attentive or unconfident and inattentive by taking the direction of face and eye into consideration. This corresponds to how many times is the candidate interacting actively, by making a firm eye contact with the interviewer. Image Processing techniques like Haar Cascade, Image filtering, Gamma Correction have been used for the detection of face and eye. Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) have been used for training and testing the images into right classes.