스마트폰 센서 데이터를 활용한 보행 활동의 그룹분류 방식에 의한 암묵적 사용자 인증

초록

Smartphones store sensitive personal information, such as addresses, financial transactions, and chat content, so making authenticationof smartphone users is increasingly important. This study proposes a system that implicitly authenticates smartphone users based onlocomotion behavior among common activities observed in everyday life. To detect locomotion activities applicable to personalauthentication during daily activities, we propose a group-classification approach that groups activities such as walking, jogging, andstair climbing into a single class. To identify locomotion activities, we present a convolutional transformer model with relative positionalencoding. Furthermore, a convolutional transformer model with the same architecture is pretrained to identify individuals, which extractsindividual features. A binary Support Vector Machine (SVM) authenticates a smartphone user with the extracted individual features. Experimental results using two public datasets demonstrate that the proposed transformer model outperforms previous studies in similarconditions in activity recognition accuracy. They also show that the proposed group-classification method is more effective for personalauthentication than the fine-classification method that subclasses locomotion behaviors.

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