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Masked Face Analysis via Multitask Deep Learning

Patel, Vatsa Sanjay
http://orcid.org/0000-0002-6688-9856
http://rave.ohiolink.edu/etdc/view?acc_num=dayton1619637677725646

Abstract Details

2021, Master of Computer Science (M.C.S.), University of Dayton, Computer Science.
Facial recognition with mask/noise has consistently been a challenging task in computer vision, which involves human wearing a facial mask. Masked Face Analysis via Multi-task deep learning is a method which will answer to many questions. In this thesis, we propose a unifying framework to simultaneously predict human age, gender, and emotions. This method is divided into three major steps; firstly, Creation of the dataset, Secondly, 3 individual classification models used for the system to learn the labelled (Age, Expression and Gender) images, Thirdly, the multi-task deep learning (MTDL) model; which takes the inputs as the data and shares their weight combined and gives the prediction of the person’s (with mask) age, expression and gender. However, this novel framework will give better output then the existing methods
Tam Nguyen (Advisor)
Ju Shen (Committee Member)
Luan Nguyen (Committee Member)
43 p.
Computer Science
Multitask learning; Multitask deep learning; Masked Face Analysis; Age prediction; Gender prediction; Expression prediction; Age, Gender and Expression; Face Mask

Recommended Citations

Citations

  • Patel, V. S. (2021). Masked Face Analysis via Multitask Deep Learning [Master's thesis, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1619637677725646

    APA Style (7th edition)

  • Patel, Vatsa. Masked Face Analysis via Multitask Deep Learning. 2021. University of Dayton, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton1619637677725646.

    MLA Style (8th edition)

  • Patel, Vatsa. "Masked Face Analysis via Multitask Deep Learning." Master's thesis, University of Dayton, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1619637677725646

    Chicago Manual of Style (17th edition)

dayton1619637677725646
36
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This open access ETD is published by University of Dayton and OhioLINK.