Speed-Independent Gait Identification for Mobile Devices

Due to the intensive use of mobile phones for different purposes, these devices usually contain confidential information which must not be accessed by another person apart from the owner of the device. Furthermore, the new generation phones commonly incorporate an accelerometer which may be used to capture the acceleration signals produced as a result of owner's gait. Nowadays, gait identification in basis of acceleration signals is being considered as a new biometric technique which allows blocking the device when another person is carrying it. Although distance based approaches as Euclidean distance or dynamic time warping have been applied to solve this identification problem, they show difficulties when dealing with gaits at different speeds. For this reason, in this paper, a method to extract an average template from instances of the gait at different velocities is presented. This method has been tested with the gait signals of 34 subjects while walking at different motion speeds (slow, normal and fast) and it has shown to improve the performance of Euclidean distance and classical dynamic time warping.

[1]  Takumi Kobayashi,et al.  Three-way auto-correlation approach to motion recognition , 2009, Pattern Recognit. Lett..

[2]  Mark S. Nixon,et al.  Automatic extraction and description of human gait models for recognition purposes , 2003, Comput. Vis. Image Underst..

[3]  B. Auvinet,et al.  Reference data for normal subjects obtained with an accelerometric device. , 2002, Gait & posture.

[4]  Tao Liu,et al.  New method for assessment of gait variability based on wearable ground reaction force sensor. , 2008, Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference.

[5]  Carla Schlatter Ellis,et al.  Using Ground Reaction Forces from Gait Analysis: Body Mass as a Weak Biometric , 2007, Pervasive.

[6]  Mikko Lindholm,et al.  Identifying people from gait pattern with accelerometers , 2005, SPIE Defense + Commercial Sensing.

[7]  Silvia Conforto,et al.  The Median Point DTW Template to Classify Upper Limb Gestures at Different Speeds , 2009 .

[8]  Larry S. Davis,et al.  EigenGait: Motion-Based Recognition of People Using Image Self-Similarity , 2001, AVBPA.

[9]  Takeshi Kurata,et al.  Personal positioning based on walking locomotion analysis with self-contained sensors and a wearable camera , 2003, The Second IEEE and ACM International Symposium on Mixed and Augmented Reality, 2003. Proceedings..

[10]  Liu Ming,et al.  Identification of Individual Walking Patterns Using Gait Acceleration , 2007, 2007 1st International Conference on Bioinformatics and Biomedical Engineering.

[11]  Einar Snekkenes,et al.  Robustness of Biometric Gait Authentication Against Impersonation Attack , 2006, OTM Workshops.

[12]  HyungDal Park,et al.  A study on the activity classification using a triaxial accelerometer , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[13]  Yuting Zhang,et al.  Accelerometer-based gait recognition via voting by signature points , 2009 .

[14]  Einar Snekkenes,et al.  Gait Recognition Using Wearable Motion Recording Sensors , 2009, EURASIP J. Adv. Signal Process..

[15]  R S Barrett,et al.  Upper body accelerations during walking in healthy young and elderly men. , 2004, Gait & posture.

[16]  Einar Snekkenes,et al.  Spoof Attacks on Gait Authentication System , 2007, IEEE Transactions on Information Forensics and Security.

[17]  T. Tamura,et al.  Classification of walking pattern using acceleration waveform in elderly people , 2000, Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (Cat. No.00CH37143).

[18]  Gerhard Tröster,et al.  Quantifying Gait Similarity: User Authentication and Real-World Challenge , 2009, ICB.

[19]  Einar Snekkenes,et al.  Improved Gait Recognition Performance Using Cycle Matching , 2010, 2010 IEEE 24th International Conference on Advanced Information Networking and Applications Workshops.

[20]  Karim Faez,et al.  Human Identification Based on Gait , 2008 .

[21]  Christopher Verplaetse,et al.  Inertial Proprioceptive Devices: Self-Motion-Sensing Toys and Tools , 1996, IBM Syst. J..

[22]  Mark S. Nixon,et al.  A floor sensor system for gait recognition , 2005, Fourth IEEE Workshop on Automatic Identification Advanced Technologies (AutoID'05).

[23]  K. Yamakoshi,et al.  A new portable device for ambulatory monitoring of human posture and walking velocity using miniature accelerometers and gyroscope , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.