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    • Thesis & Report, BSc (Computer Science and Engineering)
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    •   BracU IR
    • School of Data and Sciences (SDS)
    • Department of Computer Science and Engineering (CSE)
    • Thesis & Report, BSc (Computer Science and Engineering)
    • View Item
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    Gender detection from frontal face images

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    16341021_&_12101053_CSE.pdf (1.713Mb)
    Date
    2016
    Publisher
    BRAC University
    Author
    Alam, Mirza Mohtashim
    Rocky, Swagatam Roy
    Metadata
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    URI
    http://hdl.handle.net/10361/6422
    Abstract
    In these modern days, gender recognition from facial image has been a crucial topic. To solve such delicate problem several handy approaches are being studied in Computer Vision. However, most of these approaches hardly achieve high accuracy and precision. Lighting, illumination, proper face area detection, noise, ethnicity and various facial expressions hinder the correctness of the research. Therefore, we propose a novel gender recognition system from facial image where we first detect faces from a scene using Haar Feature Based Cascade Classifier by Paul Viola and Michael Jones with the help of Adaboost technology. The face detection goal is achieved by OpenCV. After the detection of a face and noise is reduced using Histogram equalization. Finally, Deformable Spatial Pyramid (DSP) matching algorithm is used to match the processed facial image with the knowledge base containing classified male and female frontal face images. Our proposed system pulls out better accuracy than most of the modern techniques.
    Keywords
    Edge-orienting matching; Adaptive filter; Binary pattern
     
    Description
    This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2016.
     
    Cataloged from PDF version of thesis report.
     
    Includes bibliographical references (page 41-43).
    Department
    Department of Computer Science and Engineering, BRAC University
    Collections
    • Thesis & Report, BSc (Computer Science and Engineering)

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