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기민송(Minsong Ki),최영우(Yeongwoo Choi) 한국디지털콘텐츠학회 2020 한국디지털콘텐츠학회논문지 Vol.21 No.2
We need research to detect human extreme pains using vision technologies in various environments for safety. For example, a car driver may be in a state of emergencies such as sudden pain or cardiac arrest. Therefore, we propose an emergency detection system based on human facial expressions to detect human emergent states. We organize the data for painful facial expression classes separately and propose a modified LeNet to use as a baseline. We add a resampling process to solve the noise in the training data. In addition, for Painful class with few samples and difficult to classify, we apply ring loss with softmax for clustering by facial expression class in feature space. We show accuracies of 63.3% and 60.4% for validation and testset with 8 expression classes both from 7 expression classes of FER2013[1] and an added pain class extracted from Pain Expression [4]. These results can hopefully be used to develop a system that can prevent terrible car accidents due to a sudden pain of the car drivers.