Date of Award

1-1-2015

Document Type

Dissertation

Degree Name

Ph.D.

Organizational Unit

Daniel Felix Ritchie School of Engineering and Computer Science

First Advisor

Mohammad H. Mahoor, Ph.D.

Second Advisor

Michael Kinyon

Third Advisor

Kimon Valavanis

Fourth Advisor

Nathan Sturtevant

Keywords

Action unit detection, Facial expression recognition, Multiple kernel learning, Multi-task learning, Support vector machines, Transfer learning

Abstract

Automated analysis of facial expressions has remained an interesting and challenging research topic in the field of computer vision and pattern recognition due to vast applications such as human-machine interface design, social robotics, and developmental psychology. This dissertation focuses on developing and applying transfer learning algorithms - multiple kernel learning (MKL) and multi-task learning (MTL) - to resolve the problems of facial feature fusion and the exploitation of multiple facial action units (AUs) relations in designing robust facial expression recognition systems. MKL algorithms are employed to fuse multiple facial features with different kernel functions and tackle the domain adaption problem at the kernel level within support vector machines (SVM). lp-norm is adopted to enforce both sparse and nonsparse kernel combination in our methods. We further develop and apply MTL algorithms for simultaneous detection of multiple related AUs by exploiting their inter-relationships. Three variants of task structure models are designed and investigated to obtain fine depiction of AU relations. lp-norm MTMKL and TD-MTMKL (Task-Dependent MTMKL) are group-sensitive MTL methodsthat model the co-occurrence relations among AUs. On the other hand, our proposed hierarchical multi-task structural learning (HMTSL) includes a latent layer to learn a hierarchical structure to exploit all possible AU interrelations for AU detection. Extensive experiments on public face databases show that our proposed transfer learning methods have produced encouraging results compared to several state-of-the-art methods for facial expression recognition and AU detection.

Publication Statement

Copyright is held by the author. User is responsible for all copyright compliance.

Rights Holder

Xiao Zhang

Provenance

Received from ProQuest

File Format

application/pdf

Language

en

File Size

107 p.

Discipline

Computer engineering

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