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IJMLC 2011 Vol.1(4): 409-415 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2011.V1.61

Identification of People Using Gait Biometrics

Sanjeev Sharma, Ritu Tiwari, Anupam shukla and Vikas Singh

Abstract—Gait shows a particular way or manner of moving on foot and gait recognition is the process of identifying an individual by the manner in which they walk. Gait is less unobtrusive biometric, which offers the possibility to identify people at a distance, without any interaction or co-operation from the subject; this is the property which makes it so attractive. This paper proposed new method for gait recognition. In this method, firstly binary silhouette of a walking person is detected from each frame. Secondly, feature from each frame is extracted using image processing operation. Here center of mass, step size length, and cycle length are talking as key feature. At last neural network is used for training and testing purpose. We have created different model of neural network based on hidden layer, selection of training algorithm and setting the different parameter for training. Here all experiments are done on CASIA gait database. Different groups of training and testing dataset give different results. The best recognition result for our method is 96.32%. Gait recognition is one kind of biometric technology that can be used to monitor people without their cooperation. Controlled environments such as banks, military installations and even airports need to be able to quickly detect threats and provide differing levels of access to different user groups.

Index Terms—Center of mass, Feature extraction, Gait recognition, Human identification, Neural network.

All the authors are in ABV- Indian Institute of Information Technology and management Gwalior, M. P. India. (e-mail: sanjeev.sharma1868@gmail.com; tiwariritu2@gmail.com; dranupamshukla@gmail.com; vikas.singh97@gmail.com)


Cite: Sanjeev Sharma, Ritu Tiwari, Anupam shukla and Vikas Singh, "Identification of People Using Gait Biometrics," International Journal of Machine Learning and Computing vol. 1, no. 4, pp. 409-415, 2011.

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net

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