Contributions to Head Pose Estimation methods
Fecha
2016Autor
Versión
Acceso abierto / Sarbide irekia
Tipo
Tesis doctoral / Doktoretza tesia
Impacto
|
nodoi-noplumx
|
Resumen
Head Pose Estimation (HPE) is currently a growing research field, mainly
because of the proliferation of human-computer interfaces (HCI) in the last
decade. It offers a wide variety of applications, including driver assistance
systems, pose-invariant face recognition, human behavior analysis, or popular
HCI applications such as gaze estimation systems. HCIs show an increasing
tendency to int ...
[++]
Head Pose Estimation (HPE) is currently a growing research field, mainly
because of the proliferation of human-computer interfaces (HCI) in the last
decade. It offers a wide variety of applications, including driver assistance
systems, pose-invariant face recognition, human behavior analysis, or popular
HCI applications such as gaze estimation systems. HCIs show an increasing
tendency to integrate HPE as another bridge for interaction, since it is a rich form
of communication. For instance, gaze tracking systems suffer in unconstrained
environments because they are very sensitive to head motion, and HPE has
become a key point for successful gaze estimation. This thesis thus aims to
contribute to the development of robust and accurate HPE methods based on
2D tracking of the face in videos.
With the idea of achieving a better understanding of every aspect of the HPE
process, a complete framework has been created in the first part of the thesis as a
pillar to sustain the rest of the work. This framework consists of both simulation
and realistic environments for HPE algorithm analysis. It includes the recording
of two head pose databases of videos, one with synthetically generated heads and
the other one with real subjects. They have proven to be extremely useful tools
for the purpose, and therefore we expect to make them available for the whole
scientific community.
The problem of 3D face reconstruction using only 2D images from the videos
has received special attention. A whole chapter has been devoted to the study and
comparison of different single-view and multi-view based reconstruction methods
in a controlled simulation environment. This has allowed us to isolate the 3D
model fitting problem, thus drawing several conclusions regarding the influence
of this critical part in a HPE system.
With the aim of achieving a wider impact with this thesis, the pose estimation
problem is addressed from a general perspective in which techniques that are generalizable to any kind of 3D object are proposed. Starting from a basic pose
estimation approach (2D tracking & POSIT), different alternatives have been
developed to improve performance. On the one hand, a tracking accuracy index
(TAI) calculation method has been proposed, based on invariant shape metrics
obtained from interlandmark relationships. This allows us to apply weights that
compensate for 2D tracking inaccuracies and optimize the 3D pose estimation.
On the other hand, outlier detection and outlier correction methods that aim to
improve the 2D tracking itself have been proposed, addressing the typical drifting
problem of point-tracking systems, and hence improving the 3D pose estimation
further. These global methods have then been specifically adapted to HPE and
evaluated using two head pose databases: our real database, which reflects the
expected performance in current technological conditions, and the BU database, a
widely referenced older database that allows an extensive comparison with other
state-of-the-art HPE methods. [--]
Materias
Head Pose Estimation (HPE),
Human-computer interfaces (HCI)
Departamento
Universidad Pública de Navarra. Departamento de Ingeniería Eléctrica y Electrónica /
Nafarroako Unibertsitate Publikoa. Ingeniaritza Elektrikoa eta Elektronikoa Saila