An Emotional Scene Retrieval Framework for Lifelog Videos Using Ensemble Clustering

Abstract

A lifelog video retrieval framework is proposed for the better utilization of a large amount of lifelog video data. The proposed method retrieves emotional scenes such as the scenes in which a person in the video is smiling, considering that a certain important event could happen in most of emotional scenes. The emotional scene is detected on the basis of facial expression recognition using a wide variety of facial features. The authors adopt an unsupervised learning approach called ensemble clustering in order to recognize the facial expressions because supervised learning approaches require sufficient training data, which make it quite troublesome to apply to large-scale video databases. The retrieval performance of the proposed method is evaluated by means of an emotional scene detection experiment from the viewpoints of accuracy and efficiency. In addition, a prototype retrieval system is implemented based on the proposed emotional scene detection method.