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<ArticleSet>
  <Article>
    <Journal>
      <PublisherName>IJIRCSTJournal</PublisherName>
      <JournalTitle>International Journal of Innovative Research in Computer Science and Technology</JournalTitle>
      <PISSN>I</PISSN>
      <EISSN>S</EISSN>
      <Volume-Issue>Volume 13 Issue 3</Volume-Issue>
      <PartNumber/>
      <IssueTopic>Computer Science and Engineering</IssueTopic>
      <IssueLanguage>English</IssueLanguage>
      <Season>May - June 2025</Season>
      <SpecialIssue>N</SpecialIssue>
      <SupplementaryIssue>N</SupplementaryIssue>
      <IssueOA>Y</IssueOA>
      <PubDate>
        <Year>2025</Year>
        <Month>05</Month>
        <Day>05</Day>
      </PubDate>
      <ArticleType>Computer Sciences</ArticleType>
      <ArticleTitle>An Efficient Attendance Management System for College Environments Using Machine Learning Facial Recognition Technology</ArticleTitle>
      <SubTitle/>
      <ArticleLanguage>English</ArticleLanguage>
      <ArticleOA>Y</ArticleOA>
      <FirstPage>8</FirstPage>
      <LastPage>12</LastPage>
      <AuthorList>
        <Author>
          <FirstName>Asad Zia Lari</FirstName>          
          <AuthorLanguage>English</AuthorLanguage>
          <Affiliation/>
          <CorrespondingAuthor>Y</CorrespondingAuthor>
          <ORCID/>
                      <FirstName>Faham Khan</FirstName>          
          <AuthorLanguage>English</AuthorLanguage>
          <Affiliation/>
          <CorrespondingAuthor>N</CorrespondingAuthor>
          <ORCID/>
                    <FirstName>Adeeb Ahmad</FirstName>          
          <AuthorLanguage>English</AuthorLanguage>
          <Affiliation/>
          <CorrespondingAuthor>N</CorrespondingAuthor>
          <ORCID/>
                    <FirstName>Ahmad Ali Raza</FirstName>          
          <AuthorLanguage>English</AuthorLanguage>
          <Affiliation/>
          <CorrespondingAuthor>N</CorrespondingAuthor>
          <ORCID/>
                    <FirstName>Mohammad Suaib</FirstName>          
          <AuthorLanguage>English</AuthorLanguage>
          <Affiliation/>
          <CorrespondingAuthor>N</CorrespondingAuthor>
          <ORCID/>
           
        </Author>
      </AuthorList>
      <DOI>https://doi.org/10.55524/ijircst.2025.13.3.2</DOI>
      <Abstract>Face recognition-based attendance systems have rapidly evolved as efficient solutions for automating attendance in educational and professional settings. Traditional methods-&amp;nbsp;like roll calls and RFID systems-often face challenges such as inaccuracy, time consumption, and proxy attendance issues [1]. This research presents a face recognition-based system that integrates computer vision and deep learning to ensure precise and automated attendance tracking. It captures live images, extracts facial features, and verifies identity by comparing them to a pre-stored database. The system&amp;#39;s methodology includes image acquisition, preprocessing, feature extraction using Convolutional Neural Networks (CNNs), and classification through deep learning models [2]. Its design aims to improve accuracy, reduce manual dependency, and enhance security. Experimental results demonstrate high recognition accuracy and a low false positive rate. With such potential, this system offers a transformative step in automating attendance, with a focus on security, reliability, and real-time operation. The study also discusses its benefits, limitations, and areas for future development.</Abstract>
      <AbstractLanguage>English</AbstractLanguage>
      <Keywords>Face Recognition, Deep Learning, Local Binary Pattern Histogram (LBPH), Computer Vision, Attendance Automation, Real-time Recognition, Database.</Keywords>
      <URLs>
        <Abstract>https://ijircst.org/abstract.php?article_id=1365</Abstract>
      </URLs>      
    </Journal>
  </Article>
</ArticleSet>