Journal article Open Access

Computer Vision based Detection of Partially Occluded Faces

Balasundaram A

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      <creatorName>Balasundaram A</creatorName>
      <affiliation>Assistant Professor, School of Computer Science  and Engineering, Vellore Institute of Technology (VIT-Chennai),  Chennai, India.</affiliation>
    <title>Computer Vision based Detection of Partially  Occluded Faces</title>
    <subject>Mask detection; face detection; eye detection; face occlusion; video surveillance; partial occlusion</subject>
    <subject subjectScheme="issn">2249-8958</subject>
    <subject subjectScheme="handle">C5637029320/2020©BEIESP</subject>
    <contributor contributorType="Sponsor">
      <contributorName>Blue Eyes Intelligence Engineering  &amp; Sciences Publication (BEIESP)</contributorName>
    <date dateType="Issued">2020-02-29</date>
  <resourceType resourceTypeGeneral="JournalArticle"/>
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    <relatedIdentifier relatedIdentifierType="ISSN" relationType="IsCitedBy" resourceTypeGeneral="JournalArticle">2249-8958</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.35940/ijeat.C5637.029320</relatedIdentifier>
    <rights rightsURI="">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
    <description descriptionType="Abstract">&lt;p&gt;In today&amp;rsquo;s world, security has gained utmost significance in every walk of life. With the recent advancements in image and video analytics, emphasis has been towards developing enhanced surveillance systems which perform complex tasks that include automated security incident detection, tracking and analysis in real time. The primary objective of this paper is to automatically detect the presence of any masked or occluded face in real time. A robust technique based on pivotal facial points has been developed. The paper discusses in detail how the pivotal points are observed extracted are used in discovering masked faces in real time. Analysis of this algorithm&amp;rsquo;s performance on test data sets gives positive insights for further enhancements towards occluded face detection in real time surveillance.&lt;/p&gt;</description>
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