국내 문화 예술 콘텐츠 관람객 대상 비 몰입 상태 및 감정 인식 학습 데이터셋 연구 = A Study on Training Datasets for Non-immersive States and Emotion Recognition among Domestic Cultural and Art Contents Audience

다국어 초록 (Multilingual Abstract)

In the realm of performing arts content services, the immersion and emotions experienced by consumers play a crucial role in sustaining the continuous success of content, and can be used as an indicator to determine the completeness of content and services by comparing it with the service planning intention. Recently, research using AI-based image classification models has been actively conducted to measure consumers' immersion and emotional states. However, existing datasets consist of information collected from subjects in an immersed state, making it difficult to discern non-immersive states, and are composed mostly of Westerners, so it is difficult to train the model to learn the characteristic differences based on race and culture. Therefore, this study compared and analyzed the results derived by training the model using a self-dataset constructed by preprocessing collected Korean, foreign, and mixed image datasets for emotions and non-immersion states to measure the immersion state of Korean visitors and use it to evaluate quantitative satisfaction with the content.

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