Date of Award

1-1-2015

Document Type

Dissertation

Degree Name

Ph.D.

Department

Computer Engineering

First Advisor

Mohammad H. Mahoor

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 methods

that 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.

Provenance

Recieved from ProQuest

Rights holder

Xiao Zhang

File size

107 p.

File format

application/pdf

Language

en

Discipline

Computer engineering

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