The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Secure techniques and methods for authenticating visually impaired mobile phone users
Abstract
A series of new types of frauds and threats have emerged with the increased popularity of smartphones. Studies show that smartphone users are three times more likely to become the victims of identity fraud. Though researchers have developed many well known methods for user authentication in smartphone, little has been done focusing on visually impaired mobile device users. Commonly used username-password based authentication is not suitable for such users as it is cumbersome and highly susceptible to eavesdropping. In this paper, we have proposed a comprehensive algorithmic model for detecting different physical activities, such as walking, by analyzing the accelerometer sensor data from smartphones. Our proposed scheme proves that each person's gait pattern is unique and can be used as a bio-metric data for authentication. The new tool promises to provide a feasible solution for authentication, especially for visually impaired smartphone users, free from aural and visual eavesdropping.
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The number of people with vision impairment using smartphones is increasing.
“Thanks to the rise of accessibility features and applications for mainstream devices, the number of people with vision impairment using smartphones is increasing [14,20].”
From Understanding Authentication Method Use on Mobile Devices by People with Vision Impairment (2018) · cites this paper as [20]
Citations
Cites
Activity recognition using cell phone accelerometers
Kwapisz, J. R., Weiss, G. M., & Moore, S. A. (2011) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1964897.1964918 · Cited by 2,912 · cited 1×
“showed that it is possible to identify different human activities [14].”
BackgroundPassChords: secure multi-touch authentication for blind people
Azenkot, S., Rector, K., Ladner, R., & Wobbrock, J. (2012) · Proceedings of the 14th international ACM SIGACCESS conference on Computers and accessibility · doi:10.1145/2384916.2384945 · Cited by 116 · cited 3×
“It is no wonder that a 2012 study on 13 blind smartphone users has found that none of them use the PIN based authentication system [3].”
BackgroundInfluential
Flagged first, then by citations
Research works printed but not linked: 18
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Cited by
Sorted by citations
Understanding Authentication Method Use on Mobile Devices by People with Vision Impairment
Briotto Faustino, D., & Girouard, A. (2018) · Proceedings of the 20th International ACM SIGACCESS Conference on Computers and Accessibility · doi:10.1145/3234695.3236342 · Cited by 19
“Similarly, the use of screen magnifiers by those with low vision also increases the susceptibility for visual eavesdropping [20].”
BackgroundInfluentialTowards accessible mobile pattern authentication for persons with visual impairments
Balaji, V., & Kuppusamy, K. S. (2017) · 2017 International Conference on Computational Intelligence in Data Science(ICCIDS) · doi:10.1109/iccids.2017.8272662 · Cited by 7
Metadata
- Authors
- Haque, M. M., Zawoad, S., & Hasan, R.
- Affiliations
- University of Alabama at Birmingham (US)
- Venue
- 2013 IEEE International Conference on Technologies for Homeland Security (HST)
- Publisher
- IEEE
- Published
- 2013-11
- Pages
- 735-740
- OpenAlex
- W1994702315
- Cited by
- 21
- References
- 2
- Citations by year
- 2025: 1 · 2022: 3 · 2021: 3 · 2020: 4 · 2019: 2 · 2018: 3 · 2017: 3 · 2015: 2
- FWCI
- 0.54
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Conference on Technologies for Homeland Security (HST) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › User Authentication and Security Systems
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Physical Sciences › Computer Science › Computer Networks and Communications › Advanced Authentication Protocols Security
- Keywords
- user authentication, visually impaired users, smartphone security, gait recognition, accelerometer data, physical activity detection, biometric authentication, identity fraud, eavesdropping resistance, walking detection
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