User emotion recognition from a larger pool of social network data using active learning

Abstract

The use of social networks has grown exponentially in recent years. The large amount of data available in these networks can be effectively utilized in many machine learning applications. This paper proposes a framework of an emotion recognition system that fetches huge amount of face images from the social networks into a cloud. In the cloud, the problem of the unlabeled facial images is handled by applying an active learning approach. For the feature extraction, an interlaced derivative pattern is used, while for a base classifier, an extreme learning machine is utilized. Once the emotion is recognized in the cloud, it can be shared with the end users to meet their interests. Several experiments were performed using some publicly available databases and heterogeneous images from the social networks. Experimental results showed that the proposed framework may effectively be used in the emotion recognition.