Analysis of head and torso movements for authentication

Gayathri Manogna Parimi, Partha Pratim Kundu, Vir Phoha

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Wearable computing devices have become increasingly popular and while these devices promise to improve our lives, they come with new challenges. One such device is the Google Glass from which data can be stolen easily as the touch gestures can be intercepted from a head-mounted device. This paper focuses on analyzing and combining two behavioral metrics, namely, head movement (captured through glass) and torso movement (captured through smartphone) to build a continuous authentication system that can be used on Google Glass alone or by pairing it with a smartphone. We performed a correlation analysis among the features on these two metrics and found that very little correlation exists between the features extracted from head and torso movements in most scenarios (set of activities). This led us to combine the two metrics to perform authentication. We built an authentication system using these metrics and compared the performance among different scenarios. We got EER less than 6% when authenticating a user using only the head movements in one scenario whereas the EER is less than 5% when authenticating a user using both head and torso movements in general.

Original languageEnglish (US)
Title of host publication2018 IEEE 4th International Conference on Identity, Security, and Behavior Analysis, ISBA 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-8
Number of pages8
Volume2018-January
ISBN (Electronic)9781538622483
DOIs
StatePublished - Mar 9 2018
Event4th IEEE International Conference on Identity, Security, and Behavior Analysis, ISBA 2018 - Singapore, Singapore
Duration: Jan 11 2018Jan 12 2018

Other

Other4th IEEE International Conference on Identity, Security, and Behavior Analysis, ISBA 2018
CountrySingapore
CitySingapore
Period1/11/181/12/18

Fingerprint

Torso
Head Movements
Authentication
Smartphones
scenario
Glass
Equipment and Supplies
search engine
Metric System
Gestures
Touch
Head
performance
Smartphone

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Safety, Risk, Reliability and Quality
  • Behavioral Neuroscience
  • Social Sciences (miscellaneous)

Cite this

Parimi, G. M., Kundu, P. P., & Phoha, V. (2018). Analysis of head and torso movements for authentication. In 2018 IEEE 4th International Conference on Identity, Security, and Behavior Analysis, ISBA 2018 (Vol. 2018-January, pp. 1-8). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISBA.2018.8311460

Analysis of head and torso movements for authentication. / Parimi, Gayathri Manogna; Kundu, Partha Pratim; Phoha, Vir.

2018 IEEE 4th International Conference on Identity, Security, and Behavior Analysis, ISBA 2018. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-8.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Parimi, GM, Kundu, PP & Phoha, V 2018, Analysis of head and torso movements for authentication. in 2018 IEEE 4th International Conference on Identity, Security, and Behavior Analysis, ISBA 2018. vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 1-8, 4th IEEE International Conference on Identity, Security, and Behavior Analysis, ISBA 2018, Singapore, Singapore, 1/11/18. https://doi.org/10.1109/ISBA.2018.8311460
Parimi GM, Kundu PP, Phoha V. Analysis of head and torso movements for authentication. In 2018 IEEE 4th International Conference on Identity, Security, and Behavior Analysis, ISBA 2018. Vol. 2018-January. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1-8 https://doi.org/10.1109/ISBA.2018.8311460
Parimi, Gayathri Manogna ; Kundu, Partha Pratim ; Phoha, Vir. / Analysis of head and torso movements for authentication. 2018 IEEE 4th International Conference on Identity, Security, and Behavior Analysis, ISBA 2018. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. pp. 1-8
@inproceedings{b39c9dc3847d4698be5c1f1c69dd3f69,
title = "Analysis of head and torso movements for authentication",
abstract = "Wearable computing devices have become increasingly popular and while these devices promise to improve our lives, they come with new challenges. One such device is the Google Glass from which data can be stolen easily as the touch gestures can be intercepted from a head-mounted device. This paper focuses on analyzing and combining two behavioral metrics, namely, head movement (captured through glass) and torso movement (captured through smartphone) to build a continuous authentication system that can be used on Google Glass alone or by pairing it with a smartphone. We performed a correlation analysis among the features on these two metrics and found that very little correlation exists between the features extracted from head and torso movements in most scenarios (set of activities). This led us to combine the two metrics to perform authentication. We built an authentication system using these metrics and compared the performance among different scenarios. We got EER less than 6{\%} when authenticating a user using only the head movements in one scenario whereas the EER is less than 5{\%} when authenticating a user using both head and torso movements in general.",
author = "Parimi, {Gayathri Manogna} and Kundu, {Partha Pratim} and Vir Phoha",
year = "2018",
month = "3",
day = "9",
doi = "10.1109/ISBA.2018.8311460",
language = "English (US)",
volume = "2018-January",
pages = "1--8",
booktitle = "2018 IEEE 4th International Conference on Identity, Security, and Behavior Analysis, ISBA 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - GEN

T1 - Analysis of head and torso movements for authentication

AU - Parimi, Gayathri Manogna

AU - Kundu, Partha Pratim

AU - Phoha, Vir

PY - 2018/3/9

Y1 - 2018/3/9

N2 - Wearable computing devices have become increasingly popular and while these devices promise to improve our lives, they come with new challenges. One such device is the Google Glass from which data can be stolen easily as the touch gestures can be intercepted from a head-mounted device. This paper focuses on analyzing and combining two behavioral metrics, namely, head movement (captured through glass) and torso movement (captured through smartphone) to build a continuous authentication system that can be used on Google Glass alone or by pairing it with a smartphone. We performed a correlation analysis among the features on these two metrics and found that very little correlation exists between the features extracted from head and torso movements in most scenarios (set of activities). This led us to combine the two metrics to perform authentication. We built an authentication system using these metrics and compared the performance among different scenarios. We got EER less than 6% when authenticating a user using only the head movements in one scenario whereas the EER is less than 5% when authenticating a user using both head and torso movements in general.

AB - Wearable computing devices have become increasingly popular and while these devices promise to improve our lives, they come with new challenges. One such device is the Google Glass from which data can be stolen easily as the touch gestures can be intercepted from a head-mounted device. This paper focuses on analyzing and combining two behavioral metrics, namely, head movement (captured through glass) and torso movement (captured through smartphone) to build a continuous authentication system that can be used on Google Glass alone or by pairing it with a smartphone. We performed a correlation analysis among the features on these two metrics and found that very little correlation exists between the features extracted from head and torso movements in most scenarios (set of activities). This led us to combine the two metrics to perform authentication. We built an authentication system using these metrics and compared the performance among different scenarios. We got EER less than 6% when authenticating a user using only the head movements in one scenario whereas the EER is less than 5% when authenticating a user using both head and torso movements in general.

UR - http://www.scopus.com/inward/record.url?scp=85049796521&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85049796521&partnerID=8YFLogxK

U2 - 10.1109/ISBA.2018.8311460

DO - 10.1109/ISBA.2018.8311460

M3 - Conference contribution

VL - 2018-January

SP - 1

EP - 8

BT - 2018 IEEE 4th International Conference on Identity, Security, and Behavior Analysis, ISBA 2018

PB - Institute of Electrical and Electronics Engineers Inc.

ER -