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    Por favor, use este identificador para citar o enlazar este ítem:https://uvadoc.uva.es/handle/10324/57825

    Título
    Acoustic biometric system based on preprocessing techniques and linear support vector machines
    Autor
    Val Puente, Lara delAutoridad UVA Orcid
    Izquierdo Fuente, AlbertoAutoridad UVA Orcid
    Villacorta Calvo, Juan JoséAutoridad UVA Orcid
    Raboso, Mariano
    Año del Documento
    2015
    Editorial
    MDPI
    Descripción
    Producción Científica
    Documento Fuente
    Sensors, 2015, vol. 15, n. 6, p. 14241-14260
    Abstract
    Drawing on the results of an acoustic biometric system based on a MSE classifier, a new biometric system has been implemented. This new system preprocesses acoustic images, extracts several parameters and finally classifies them, based on Support Vector Machine (SVM). The preprocessing techniques used are spatial filtering, segmentation—based on a Gaussian Mixture Model (GMM) to separate the person from the background, masking—to reduce the dimensions of images—and binarization—to reduce the size of each image. An analysis of classification error and a study of the sensitivity of the error versus the computational burden of each implemented algorithm are presented. This allows the selection of the most relevant algorithms, according to the benefits required by the system. A significant improvement of the biometric system has been achieved by reducing the classification error, the computational burden and the storage requirements.
    Materias Unesco
    33 Ciencias Tecnológicas
    12 Matemáticas
    Palabras Clave
    Acoustic biometric system
    Acoustic images
    Preprocessing techniques
    Support Vector Machine (SVM)
    ISSN
    1424-8220
    Revisión por pares
    SI
    DOI
    10.3390/s150614241
    Version del Editor
    https://www.mdpi.com/1424-8220/15/6/14241
    Propietario de los Derechos
    © 2015 The Author(s)
    Idioma
    eng
    URI
    https://uvadoc.uva.es/handle/10324/57825
    Tipo de versión
    info:eu-repo/semantics/publishedVersion
    Derechos
    openAccess
    Collections
    • DEP07 - Artículos de revista [27]
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