QoE-Oriented Multimedia Assessment: A Facial Expression Recognition Approach

Abstract

Multimedia services are predominant in current wireless networks and are becoming ubiquitous in the upcoming 5G era in which the video quality of experience (QoE) is a fundamental metric. However, no widely accepted QoE model exists due to its subjective nature. This paper proposes a framework for quantifying the QoE of multimedia content based on the facial expression approach, which can directly reflect the end users’ intrinsic attitudes toward the services. To achieve this objective, a face database is established containing over one thousand videos and serves as a dataset for the subsequent experience mining. The semi-supervised clustering method proposed in this paper is applied to calculate the video experience scores and achieves 8% higher average test accuracy than other prevailing methods. Extensive experimental results show that our approach can accurately reveal the user's experience toward video contents and is expected to become a valid and useful QoE model.