@article{(Open Science Index):https://publications.waset.org/pdf/2604,
	  title     = {Multi-View Neural Network Based Gait Recognition},
	  author    = {Saeid Fazli and  Hadis Askarifar and  Maryam Sheikh Shoaie},
	  country	= {},
	  institution	= {},
	  abstract     = {Human identification at a distance has recently gained
growing interest from computer vision researchers. Gait recognition
aims essentially to address this problem by identifying people based
on the way they walk [1]. Gait recognition has 3 steps. The first step
is preprocessing, the second step is feature extraction and the third
one is classification. This paper focuses on the classification step that
is essential to increase the CCR (Correct Classification Rate).
Multilayer Perceptron (MLP) is used in this work. Neural Networks
imitate the human brain to perform intelligent tasks [3].They can
represent complicated relationships between input and output and
acquire knowledge about these relationships directly from the data
[2]. In this paper we apply MLP NN for 11 views in our database and
compare the CCR values for these views. Experiments are performed
with the NLPR databases, and the effectiveness of the proposed
method for gait recognition is demonstrated.},
	    journal   = {International Journal of Psychological and Behavioral Sciences},
	  volume    = {4},
	  number    = {7},
	  year      = {2010},
	  pages     = {270 - 274},
	  ee        = {https://publications.waset.org/pdf/2604},
	  url   	= {https://publications.waset.org/vol/43},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 43, 2010},