Impressive Scene Detection from Lifelog Videos by Unsupervised Facial Expression Recognition

In order to retrieve impressive scenes from life log videos, we propose an emotional scene detection method based on facial expression recognition. Many researches about facial expression recognition focus on discriminating typical facial expressions such as happiness, sadness and surprise. But they are not suitable for life log videos because more complicated or subtle facial expressions are frequently observed in them. The proposed method tries to solve this problem by constructing a facial expression recognition model based on unsupervised learning. It discriminates facial expressions and detects emotional scenes by a clustering approach using facial features based on the positional relationships of several facial feature points. This approach is fully flexible to detect various facial expressions from life log videos because it does not need to predefine the facial expressions. The detection performance of the proposed method is evaluated in terms of detection accuracy and efficiency through the emotional scene detection experiments.