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.
| Original language | English (US) |
|---|---|
| Title of host publication | 2019 IEEE International Conference on Communications, ICC 2019 - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9781538680889 |
| DOIs | |
| State | Published - May 1 2019 |
| Externally published | Yes |
| Event | 2019 IEEE International Conference on Communications, ICC 2019 - Shanghai, China Duration: May 20 2019 → May 24 2019 |
Publication series
| Name | IEEE International Conference on Communications |
|---|---|
| Volume | 2019-May |
| ISSN (Print) | 1550-3607 |
Conference
| Conference | 2019 IEEE International Conference on Communications, ICC 2019 |
|---|---|
| Country | China |
| City | Shanghai |
| Period | 5/20/19 → 5/24/19 |
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ASJC Scopus subject areas
- Computer Networks and Communications
- Electrical and Electronic Engineering
Cite this
FMHash : Deep Hashing of In-Air-Handwriting for User Identification. / Lu, Duo; Huang, DIjiang; Rai, Anshul.
2019 IEEE International Conference on Communications, ICC 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. 8761508 (IEEE International Conference on Communications; Vol. 2019-May).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - FMHash
T2 - Deep Hashing of In-Air-Handwriting for User Identification
AU - Lu, Duo
AU - Huang, DIjiang
AU - Rai, Anshul
PY - 2019/5/1
Y1 - 2019/5/1
N2 - 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.
AB - 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.
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U2 - 10.1109/ICC.2019.8761508
DO - 10.1109/ICC.2019.8761508
M3 - Conference contribution
AN - SCOPUS:85070187980
T3 - IEEE International Conference on Communications
BT - 2019 IEEE International Conference on Communications, ICC 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
ER -