Abstract
Biometric authentication mechanisms are excellent alternatives to often inconvenient interaction authentication methods such as PIN numbers in mobile devices. This research introduces the new concept of using gait signature metrics for biometric authentication. This procedure verifies each subject using only acceleration. We first use a single wireless sensor device to collect data on subjects' gait patterns. By dividing each gait cycle into an Acceleration Phase and a Deceleration Phase, we derive seven periodic and characteristic gait signature metrics. Gait signature metrics can be classified as acceleration metrics, deceleration metrics, and ratio metric. Acceleration metrics represent a degree of dynamic activity when heel-strike actions and mid-stance actions occur, whereas deceleration metrics measure a degree of dynamic activity when mid-stance actions and successive heel-strike of other foot. The last metric, ratio metric, present the relationship between the acceleration metrics and the deceleration metrics. Using the gait signature metrics, we succeeded in differentiating each subject with 100% accuracy.
| Original language | English (US) |
|---|---|
| Title of host publication | 2014 IEEE 11th Consumer Communications and Networking Conference, CCNC 2014 |
| Publisher | IEEE Computer Society |
| Pages | 596-601 |
| Number of pages | 6 |
| ISBN (Print) | 9781479923557 |
| State | Published - Jan 1 2014 |
| Event | 2014 IEEE 11th Consumer Communications and Networking Conference, CCNC 2014 - Las Vegas, NV, United States Duration: Jan 10 2014 → Jan 13 2014 |
Publication series
| Name | 2014 IEEE 11th Consumer Communications and Networking Conference, CCNC 2014 |
|---|
Conference
| Conference | 2014 IEEE 11th Consumer Communications and Networking Conference, CCNC 2014 |
|---|---|
| Country | United States |
| City | Las Vegas, NV |
| Period | 1/10/14 → 1/13/14 |
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New gait metrics for biometric authentication using a 3-axis acceleration. / Youn, Ik Hyun; Choi, Sangil; LeMay, Richelle; Bertelsen, Douglas; Youn, Jong Hoon.
2014 IEEE 11th Consumer Communications and Networking Conference, CCNC 2014. IEEE Computer Society, 2014. p. 596-601 6940501 (2014 IEEE 11th Consumer Communications and Networking Conference, CCNC 2014).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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TY - GEN
T1 - New gait metrics for biometric authentication using a 3-axis acceleration
AU - Youn, Ik Hyun
AU - Choi, Sangil
AU - LeMay, Richelle
AU - Bertelsen, Douglas
AU - Youn, Jong Hoon
PY - 2014/1/1
Y1 - 2014/1/1
N2 - Biometric authentication mechanisms are excellent alternatives to often inconvenient interaction authentication methods such as PIN numbers in mobile devices. This research introduces the new concept of using gait signature metrics for biometric authentication. This procedure verifies each subject using only acceleration. We first use a single wireless sensor device to collect data on subjects' gait patterns. By dividing each gait cycle into an Acceleration Phase and a Deceleration Phase, we derive seven periodic and characteristic gait signature metrics. Gait signature metrics can be classified as acceleration metrics, deceleration metrics, and ratio metric. Acceleration metrics represent a degree of dynamic activity when heel-strike actions and mid-stance actions occur, whereas deceleration metrics measure a degree of dynamic activity when mid-stance actions and successive heel-strike of other foot. The last metric, ratio metric, present the relationship between the acceleration metrics and the deceleration metrics. Using the gait signature metrics, we succeeded in differentiating each subject with 100% accuracy.
AB - Biometric authentication mechanisms are excellent alternatives to often inconvenient interaction authentication methods such as PIN numbers in mobile devices. This research introduces the new concept of using gait signature metrics for biometric authentication. This procedure verifies each subject using only acceleration. We first use a single wireless sensor device to collect data on subjects' gait patterns. By dividing each gait cycle into an Acceleration Phase and a Deceleration Phase, we derive seven periodic and characteristic gait signature metrics. Gait signature metrics can be classified as acceleration metrics, deceleration metrics, and ratio metric. Acceleration metrics represent a degree of dynamic activity when heel-strike actions and mid-stance actions occur, whereas deceleration metrics measure a degree of dynamic activity when mid-stance actions and successive heel-strike of other foot. The last metric, ratio metric, present the relationship between the acceleration metrics and the deceleration metrics. Using the gait signature metrics, we succeeded in differentiating each subject with 100% accuracy.
UR - http://www.scopus.com/inward/record.url?scp=84941004457&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84941004457&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84941004457
SN - 9781479923557
T3 - 2014 IEEE 11th Consumer Communications and Networking Conference, CCNC 2014
SP - 596
EP - 601
BT - 2014 IEEE 11th Consumer Communications and Networking Conference, CCNC 2014
PB - IEEE Computer Society
ER -