University of Illinois Urbana-Champaign

Identifying facial landmarks, action units and emotions using deep networks

Prabhu, Namrata

Content Files
PRABHU-THESIS-2016.pdf

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https://hdl.handle.net/2142/92967

Description

Title
Identifying facial landmarks, action units and emotions using deep networks
Author(s)
Prabhu, Namrata
Issue Date
2016-07-20
Director of Research (if dissertation) or Advisor (if thesis)
Hoiem, Derek
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Date of Ingest
2016-11-10T18:35:29Z
Keyword(s)
  • Convolutional Neural Networks (CNN)
  • Facial Landmarks
  • Expressions
  • Action Units
Abstract
The goal of this thesis it to use deep neural networks, specifically Convolutional Neural Networks (CNNs) to predict facial landmarks, facial action units and emotions and to study the results of intermediate experiments while doing so. Learning the different features of facial images has always been a difficult task and primarily involves using hand-crafted features which would almost definitely ignore some information related to the different dynamics of facial features. We train our network model using the raw facial images and study its effectiveness in predicting facial landmarks, action units and emotions. In this thesis we learnt that CNNs are highly effective in predicting facial landmarks and AUs, mainly because of their ability to learn features from raw images. We also established that feature sets which can effectively outline the different properties of a face are more useful in classifying facial emotions than either images or facial landmarks.
Graduation Semester
2016-08
Type of Resource
text
Permalink
http://hdl.handle.net/2142/92967
Copyright and License Information
Copyright 2016 Namrata Prabhu

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Graduate Dissertations and Theses at Illinois PRIMARY
Graduate Theses and Dissertations at Illinois