DocumentCode :
2727990
Title :
Dynamic-footprint based person identification using mat-type pressure sensor
Author :
Jin-Woo Jung ; Bien, Z. ; Sang-Wang Lee ; Sato, T.
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., Korea Adv. Inst. of Sci. & Technol., Daejeon, South Korea
Volume :
3
fYear :
2003
fDate :
17-21 Sept. 2003
Firstpage :
2937
Abstract :
Many diverse methods have been developing in the field of biometric identification as human-friendliness has been emphasized in the intelligent system´s area. And one of emerging method is to use human walking behavior. But, in the previous methods based on human gait, stable somewhat long-term walking data are an essential condition for person recognition. Therefore, these methods are difficult to cope with various change of walking velocity which may be generated frequently during real walking. In this paper, we suggest a new method which uses just one-step walking data from mat-type pressure sensor. When a human walk through the pressure sensor, we get quantized COP (center of pressure) trajectory and HMM (hidden Markov model) is used to make probability models for user´s each foot. And then, HMMs for two feet are combined for better performance by Levenberg-Marquart learning method. Finally, we prove the usefulness of the suggested method using 8 people recognition experiments.
Keywords :
biomechanics; biometrics (access control); hidden Markov models; learning (artificial intelligence); pressure sensors; probability; Levenberg-Marquart learning; biometric identification; center of pressure trajectory; footprint; hidden Markov model; human walking behavior; mat-type pressure sensor; person identification; Biometrics; Face recognition; Fingerprint recognition; Hidden Markov models; Identification of persons; Learning systems; Legged locomotion; Sensor systems;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society, 2003. Proceedings of the 25th Annual International Conference of the IEEE
Conference_Location :
Cancun
ISSN :
1094-687X
Print_ISBN :
0-7803-7789-3
Type :
conf
DOI :
10.1109/IEMBS.2003.1280533
Filename :
1280533
Link To Document :
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