Evaluating facial recognition services as interaction technique for recommender systems

Abstract

Recommender systems are tools and techniques to assist users in the content selection process thereby coping with the problem of information overload. For recommender systems, user authentication and feedback gathering are of crucial importance. However, the typical user authentication with username / password and feedback method with a star rating system are not user friendly and often bypassed. This article proposes an alternative method for user authentication based on facial recognition and an automatic feedback gathering method by detecting various face characteristics such as emotions. We studied the use case of video watching. Photos made with the front-facing camera of a tablet, smartphone, or smart TV are used as input of a facial recognition service. The persons in front of the screen can be identified. During video watching, implicit feedback for the video content is automatically gathered through emotion recognition, attention measurements, and behavior analysis. An evaluation with a test panel showed that the recognized emotions are correlated with the user’s star ratings and that happiness can be most accurately detected. So as the main contribution, this article indicates that emotion recognition might be used as an alternative feedback mechanism for recommender systems.