Acoustic Biometric System Based on Preprocessing Techniques and Linear Support Vector Machines
Identifieur interne : 000B72 ( Ncbi/Curation ); précédent : 000B71; suivant : 000B73Acoustic Biometric System Based on Preprocessing Techniques and Linear Support Vector Machines
Auteurs : Lara Del Val [Espagne] ; Alberto Izquierdo-Fuente ; Juan J. Villacorta ; Mariano RabosoSource :
- Sensors (Basel, Switzerland) [ 1424-8220 ] ; 2015.
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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
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DOI: 10.3390/s150614241
PubMed: 26091392
PubMed Central: 4507697
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Villacorta</name><affiliation><nlm:aff id="af2-sensors-15-14241">Departamento de Teoría de la Señal y Comunicaciones e Ingeniería Telemática, Universidad de Valladolid, Paseo Belén 15, 47011 Valladolid, Spain; E-Mails:<email>alberto.izquierdo@tel.uva.es</email> (A.I.-F.);<email>juavil@tel.uva.es</email> (J.J.V.)</nlm:aff></affiliation></author><author><name sortKey="Raboso, Mariano" sort="Raboso, Mariano" uniqKey="Raboso M" first="Mariano" last="Raboso">Mariano Raboso</name><affiliation><nlm:aff id="af3-sensors-15-14241">E.U. Informática, Universidad Pontificia de Salamanca, Calle Compañía 5, 37002 Salamanca, Spain; E-Mail:<email>mrabosoma@upsa.es</email></nlm:aff></affiliation></author></analytic><series><title level="j">Sensors (Basel, Switzerland)</title><idno type="eISSN">1424-8220</idno><imprint><date when="2015">2015</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Acoustics</term><term>Biometry (methods)</term><term>Female</term><term>Humans</term><term>Image Processing, Computer-Assisted (methods)</term><term>Male</term><term>Signal Processing, Computer-Assisted</term><term>Support Vector Machine</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Acoustique</term><term>Biométrie ()</term><term>Femelle</term><term>Humains</term><term>Machine à vecteur de support</term><term>Mâle</term><term>Traitement d'image par ordinateur ()</term><term>Traitement du signal assisté par ordinateur</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Biometry</term><term>Image Processing, Computer-Assisted</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Acoustics</term><term>Female</term><term>Humans</term><term>Male</term><term>Signal Processing, Computer-Assisted</term><term>Support Vector Machine</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Acoustique</term><term>Biométrie</term><term>Femelle</term><term>Humains</term><term>Machine à vecteur de support</term><term>Mâle</term><term>Traitement d'image par ordinateur</term><term>Traitement du signal assisté par ordinateur</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>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 <italic>versus</italic> 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.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Jain, A" uniqKey="Jain A">A. 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