Analysis of Intelligent movie recommender system from facial expression

Abstract

The advent of machine learning provides a new angle to solver different real-time challenges in business and research applications. In general, machine learning is nothing but the greater conversion of traditional mathematics application. Machine learning models play a significant role in facial recognition applications. Facial recognition is used in many real-time applications like security systems, automated attendance, offices etc. One of the important applications of machine learning models is movie recommendation using facial detection. This has been carried out by capturing the emotion to save time of the user rather than searching individual movies. Some relevant research works are carried out based on the attentional convolutional neural (recognizes each facial micro expression) and recommender system has been implemented to provide either the movies or song based on the output received from previous input i.e. CNN. Another work implemented for facial recognition using decision trees, boosting algorithms has been proven to be very inefficient compared to CNN. So, CNN seems more appropriate to obtain the best possible accuracy. Also, the combination of both the types of recommendation system i.e. content based and collaborative filtering offers more power for recommender system.