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Commenced in January 2007 Frequency: Monthly Edition: International Publications Count: 29023


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2604
Multi-View Neural Network Based Gait Recognition
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.
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References:

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[2] Heesung Lee, Sungjun Hong, Euntai Kim,''Neural network ensemble with probabilistic fusion and its application to gait recognition'', Biometrics Engineering Research Center, School of Electrical and Electronic Engineering, Yonsei University, C613, Sinchon-dong, Seodaemun-gu, Seoul 120-749, Republic of Korea.
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