TY - JOUR
T1 - The largest inertial sensor-based gait database and performance evaluation of gait-based personal authentication
AU - Ngo, Thanh Trung
AU - Makihara, Yasushi
AU - Nagahara, Hajime
AU - Mukaigawa, Yasuhiro
AU - Yagi, Yasushi
N1 - Funding Information:
This work was partially supported by JSPS Grant-in-Aid for Scientific Research(S) Grant Number 21220003 , and the JST CREST “Behavior Understanding based on Intention-Gait Model” project.
PY - 2014/1
Y1 - 2014/1
N2 - This paper presents the largest inertial sensor-based gait database in the world, which is made open to the research community, and its application to a statistically reliable performance evaluation for gait-based personal authentication. We construct several datasets for both accelerometer and gyroscope of three inertial measurement units and a smartphone around the waist of a subject, which include at most 744 subjects (389 males and 355 females) with ages ranging from 2 to 78 years. The database has several advantages: a large number of subjects with a balanced gender ratio, variations of sensor types, sensor locations, and ground slope conditions. Therefore, we can reliably analyze the dependence of gait authentication performance on a number of factors such as gender, age group, sensor type, ground condition, and sensor location. The results with the latest existing authentication methods provide several insights for these factors.
AB - This paper presents the largest inertial sensor-based gait database in the world, which is made open to the research community, and its application to a statistically reliable performance evaluation for gait-based personal authentication. We construct several datasets for both accelerometer and gyroscope of three inertial measurement units and a smartphone around the waist of a subject, which include at most 744 subjects (389 males and 355 females) with ages ranging from 2 to 78 years. The database has several advantages: a large number of subjects with a balanced gender ratio, variations of sensor types, sensor locations, and ground slope conditions. Therefore, we can reliably analyze the dependence of gait authentication performance on a number of factors such as gender, age group, sensor type, ground condition, and sensor location. The results with the latest existing authentication methods provide several insights for these factors.
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U2 - 10.1016/j.patcog.2013.06.028
DO - 10.1016/j.patcog.2013.06.028
M3 - Article
AN - SCOPUS:84885021136
SN - 0031-3203
VL - 47
SP - 228
EP - 237
JO - Pattern Recognition
JF - Pattern Recognition
IS - 1
ER -