<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>1815-5928</journal-id>
<journal-title><![CDATA[Ingeniería Electrónica, Automática y Comunicaciones]]></journal-title>
<abbrev-journal-title><![CDATA[EAC]]></abbrev-journal-title>
<issn>1815-5928</issn>
<publisher>
<publisher-name><![CDATA[Universidad Tecnológica de La Habana José Antonio Echeverría, Cujae]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1815-59282019000300028</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Normalización de imágenes de orejas para el reconocimiento biométrico]]></article-title>
<article-title xml:lang="en"><![CDATA[Ears Images Normalization for Biometric Recognition]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Grenot Castellano]]></surname>
<given-names><![CDATA[Elaine]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Carballea Alonso]]></surname>
<given-names><![CDATA[Ernesto]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Silva Mata]]></surname>
<given-names><![CDATA[Francisco José]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Martínez Díaz]]></surname>
<given-names><![CDATA[Yoanna]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Sánchez Rivero]]></surname>
<given-names><![CDATA[Rubén]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Centro de Aplicaciones de Tecnologías de Avanzada  ]]></institution>
<addr-line><![CDATA[ La Habana]]></addr-line>
<country>Cuba</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2019</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2019</year>
</pub-date>
<volume>40</volume>
<numero>3</numero>
<fpage>28</fpage>
<lpage>38</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S1815-59282019000300028&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S1815-59282019000300028&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S1815-59282019000300028&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN La alineación de imágenes es un paso crucial en los sistemas de reconocimiento basado en imágenes biométricas, ya que facilita su análisis y comparación. En particular, las imágenes de la oreja pueden presentar problemas de oclusión por el pelo, pendientes o sombreros. Estas oclusiones disminuyen la zona de reconocimiento de este objeto biométrico; así como la variación de la iluminación en el proceso de captura aumenta la dificultad de extracción de rasgos o puntos fiduciales. La información mutua es una técnica estadística basada en la información proporcionada por la distribución de las intensidades en las imágenes. El método propuesto basado en esta técnica no depende de los valores reales de los píxeles sino de cómo están distribuidos en la imagen, esto le permite enfrentar problemas de oclusión, efectos de la iluminación, y ruido. En este trabajo, mostraremos la utilidad de la información mutua entre las imágenes de orejas para su alineación. Para ello, se realizaron varios experimentos comparando este método con otros de carácter estadístico, como los de PCA y otros basados en puntos fiduciales, tales como el proceso de interpolación y muestreo warping. La evaluación de resultados se realiza mediante secuencias de reconocimiento utilizando la representación funcional de la oreja.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT Image alignment is a crucial step in recognition systems based on biometric images, since it facilitates image analysis and comparison. In particular, ear biometric images may present occlusion problems because of hair, earrings or hats. These occlusions decrease the ear recognition zone; as well as the variation of lighting in the capture process increases difficulty of extracting traits or fiducial points. Mutual information is a statistical technique based on the information provided by the distribution of the intensities in images. The proposed method based on this technique does not depend on the actual values of the pixels but on how they are distributed in the image, this allows you to deal with problems of occlusion, effects of lighting, and noise. In this paper, we will show the usefulness of mutual information between ear images for their alignment. To do this, several experiments were conducted comparing this method with others of a statistical nature, such as those in PCA and other based on fiducial points, such as warping. The evaluation of results is made through recognition sequences by representing the ear with functions.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Oreja]]></kwd>
<kwd lng="es"><![CDATA[alineación]]></kwd>
<kwd lng="es"><![CDATA[información mutua]]></kwd>
<kwd lng="en"><![CDATA[Ear]]></kwd>
<kwd lng="en"><![CDATA[alignment]]></kwd>
<kwd lng="en"><![CDATA[mutual information]]></kwd>
</kwd-group>
</article-meta>
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