Abstract:Recommender systems have proven their usefulness as a tool to cope with the information overload problem for many online services offering movies, books, or music. Recommender systems rely on identifying individual users and deducing their preferences from the feedback they provide on the content. To automate this user identification and feedback process for TV applications, we propose a solution based on face detection and recognition services. These services output useful information such as an estimation of the age, the gender, and the mood of the person. Demographic characteristics (age and gender) are used to classify the user and cope with the cold start problem. Detected smiles and emotions are used as an automatic feedback mechanism during content consumption. Accurate results are obtained in case of a frontal view of the face. Head poses deviating from a frontal view and suboptimal illumination conditions may hinder face detection and recognition, especially if parts of the face, such as eyes or mouth are not sufficiently visible.