Gait Recognition System Using Support Vector Machine and Neural Network.

Gait is a particular way or manner of moving on foot, whereas, 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 fro...

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Publicado en:Journal of Basrah Researches (Sciences) Vol. 40; no. 4A; pp. 68 - 79
Autores principales: Hassin AL-Asadi, Abbas Hanon, Talal AL-Abadi, Entesar Barges
Formato: Artículo
Publicado: Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) 2014
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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        atl: Gait Recognition System Using Support Vector Machine and Neural Network.
      aug:
        au:
          Hassin AL-Asadi, Abbas Hanon
          Talal AL-Abadi, Entesar Barges
        affil: Departtment of Computer Science, College of Science, University of Basra
      su:
        Support vector machines
        Artificial neural networks
        Pattern recognition systems
        Biometric identification
        Algorithms
        MatLab (Computer software)
      sug:
        subj:
          Support vector machines
          Artificial neural networks
          Pattern recognition systems
          Biometric identification
          Algorithms
          MatLab (Computer software)
      keyword:
        Eigen Background
        Gait
        Gait Recognition
        Neural Network.
        Support Vector Machine
      ab: Gait is a particular way or manner of moving on foot, whereas, 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 a new method for gait recognition. In this method, firstly binary silhouette of a walking person is detected from each frame by using Eigen background method. Secondly, gait cycle is detected by using aspect ratio method; thirdly, features from each frame in gait cycle are extracted by combine two methods: model base and model free. Finally, support vector machine and neural network are used for training and testing purposes. Experimental results show that our algorithm is robust and efficient, project is designed by Matlab.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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