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• W2807602384 abstract "Many mobile systems and wearable devices, such as Virtual Reality (VR) or Augmented Reality (AR) headsets, lack a keyboard or touchscreen to type an ID and password for signing into a virtual website. However, they are usually equipped with gesture capture interfaces to allow the user to interact with the system directly with hand gestures. Although gesture-based authentication has been well-studied, less attention is paid to the gesture-based user identification problem, which is essentially an input method of account ID and an efficient searching and indexing method of a database of gesture signals. In this paper, we propose FMHash (i.e., Finger Motion Hash), a user identification framework that can generate a compact binary hash code from a piece of in-air-handwriting of an ID string. This hash code enables indexing and fast search of a large account database using the in-air-handwriting by a hash table. To demonstrate the effectiveness of the framework, we implemented a prototype and achieved >99.5% precision and >92.6% recall with exact hash code match on a dataset of 200 accounts collected by us. The ability of hashing in-air-handwriting pattern to binary code can be used to achieve convenient sign-in and sign-up with in-air-handwriting gesture ID on future mobile and wearable systems connected to the Internet." @default.
• W2807602384 created "2018-06-13" @default.
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• W2807602384 date "2018-06-09" @default.
• W2807602384 modified "2023-09-25" @default.
• W2807602384 title "FMHash: Deep Hashing of In-Air-Handwriting for User Identification" @default.
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• W2807602384 doi "https://doi.org/10.48550/arxiv.1806.03574" @default.
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