Identifying facial landmarks, action units and emotions using deep networks

Prabhu, Namrata

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

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

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

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

Copyright and License Information

Copyright 2016 Namrata Prabhu

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