여행 사진을 통한 디지털 자아 확장: AI 기반 SNS 여행 사진 얼굴 분석

초록

This study seeks to identify travelers’ self-identity as embedded in travel photos posted on the Instagram platform. Through social networking services(SNS), travelers’ identities are integrated into the virtual digital world. To explore this phenomenon, the study distinguishes between the digital self and the actual self by analyzing physical cues in digital self-representations, such as facial features, smiling expressions, and photos in which the face is not exposed. Four AI-based methods were employed. First, a bounding box detection model was used to identify faces in the photos. Second, a face landmark model, implemented using Google’s MediaPipe algorithm, was applied to detect 478 facial muscle position values. Third, the principles of the Facial Action Coding System(FACS) were used to code and interpret key facial muscle movements. The findings reveal evidence of dematerialization, wherein SNS travel photos function as possessions of the extended digital self. Additionally, the study offers insights into facial expression behaviors in social media photo uploads and proposes AI-based methodological approaches for future tourism and identity research.

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