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    • Thesis & Report, BSc (Computer Science and Engineering)
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    •   BracU IR
    • School of Engineering and Computer Science (SECS)
    • Department of Computer Science and Engineering (CSE)
    • Thesis & Report, BSc (Computer Science and Engineering)
    • View Item
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    Using machine learning for lie detection: classification of human visual morphology

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    14101005,17241023,17241022_CSE.pdf (1.377Mb)
    Date
    2018-04-25
    Publisher
    BRAC University
    Author
    Azhan, Samin
    Zaman, Anik
    Bhuiyan, Monjur Rakib
    Metadata
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    URI
    http://hdl.handle.net/10361/10144
    Abstract
    Though there exists different methods of detecting lies, machine learning can be used to create a reliable and more efficient system to detect lies. This thesis proposes a method of using nonverbal human behaviors to detect lies using machine learning. This lie detection system is based on micro-expressions of human beings which uses Facial Landmark Detection System and Azure Machine Learning. Movements of individual facial muscles are recorded while a person answers some certain questions. By using the two algorithms Two-Class Support Vector Machine and Linear Regression, we attempted to create a machine that can detect lies. We reached an accuracy of approximately 76.2%.
    Keywords
    Machine learning; Lie detection; Visual morphology; Human
     
    Description
    This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018.
     
    Cataloged from PDF version of thesis.
     
    Includes bibliographical references (page 35).
    Department
    Department of Computer Science and Engineering, BRAC University
    Collections
    • Thesis & Report, BSc (Computer Science and Engineering)

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