IEEE Transactions on Pattern Analysis and Machine Intelligence
Smart Environments: Technology, Protocols and Applications (Wiley Series on Parallel and Distributed Computing)
Gait recognition using wearable motion recording sensors
EURASIP Journal on Advances in Signal Processing - Special issue on recent advances in biometric systems: a signal processing perspective
Gait analysis for human identification
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Improved Gait Recognition Performance Using Cycle Matching
WAINA '10 Proceedings of the 2010 IEEE 24th International Conference on Advanced Information Networking and Applications Workshops
WSEAS Transactions on Signal Processing
Unobtrusive User-Authentication on Mobile Phones Using Biometric Gait Recognition
IIH-MSP '10 Proceedings of the 2010 Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing
Improved Cycle Detection for Accelerometer Based Gait Authentication
IIH-MSP '10 Proceedings of the 2010 Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing
Phase registration of a single quasi-periodic signal using self dynamic time warping
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
Spoof Attacks on Gait Authentication System
IEEE Transactions on Information Forensics and Security - Part 2
Phase registration in a gallery improving gait authentication
IJCB '11 Proceedings of the 2011 International Joint Conference on Biometrics
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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.