The University of Southampton
University of Southampton Institutional Repository

On using gait to enhance face extraction for visual surveillance

On using gait to enhance face extraction for visual surveillance
On using gait to enhance face extraction for visual surveillance
Visual surveillance finds increasing deployment for monitoring urban environments. Operators need to be able to determine identity from surveillance images and often use face recognition for this purpose. Unfortunately, the quality of the recorded imagery can be insufficient for this task. This study describes a programme of research aimed to ameliorate this limitation. Many face biometrics systems use controlled environments where subjects are viewed directly facing the camera. This is less likely to occur in surveillance environments, so it is necessary to handle pose variations of the human head, low frame rate, and low resolution input images. We describe the first use of gait to enable face acquisition and recognition, by analysis of 3D head motion and gait trajectory, with super-resolution analysis. The face extraction procedure consists of three stages: i) head pose estimation by a 3D ellipsoidal model; ii) face region extraction by using a 2D or a 3D gait trajectory; and iii) frontal face extraction and reconstruction by estimating head pose and using super-resolution techniques. The head pose is estimated by using a 3D ellipsoidal model and non-linear optimisation. Region- and distance-based feature refinement methods are used and a direct mapping from the 2D image coordinate to the object coordinate is developed. In face region extraction the potential face region is extracted based on the 2D gait trajectory model when a person walks towards a camera. We model a looming field and show how this field affects the image sequences of the human walking. By fitting a 2D gait trajectory model the face region can then be tracked. For the general case of the human walking a 3D gait trajectory model and heel strike positions are used to extract the face region in 3D space. Wavelet decomposition is used to detect the gait cycle and a new heel strike detection method is developed. In face extraction a high resolution frontal face image is reconstructed with low resolution face images by analysing super-resolution. Based on the head pose and 3D ellipsoidal model the invalid low resolution face images are filtered and the frontal view face is reconstructed. By adapting the existing super-resolution the high resolution frontal face image can be synthesised, which is demonstrated to be suitable for face recognition. The contributions of this research include the construction of a 3D model for pose estimation from planar imagery and the first use of gait information to enhance the face extraction and recognition process allowing for deployment in surveillance scenarios.
Jung, Sung Uk
7d4ceed9-1bc6-4740-a398-b81a670438ba
Jung, Sung Uk
7d4ceed9-1bc6-4740-a398-b81a670438ba
Nixon, Mark S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12

Jung, Sung Uk (2012) On using gait to enhance face extraction for visual surveillance. University of Southampton, Faculty of Physical and Applied Sciences, Doctoral Thesis, 119pp.

Record type: Thesis (Doctoral)

Abstract

Visual surveillance finds increasing deployment for monitoring urban environments. Operators need to be able to determine identity from surveillance images and often use face recognition for this purpose. Unfortunately, the quality of the recorded imagery can be insufficient for this task. This study describes a programme of research aimed to ameliorate this limitation. Many face biometrics systems use controlled environments where subjects are viewed directly facing the camera. This is less likely to occur in surveillance environments, so it is necessary to handle pose variations of the human head, low frame rate, and low resolution input images. We describe the first use of gait to enable face acquisition and recognition, by analysis of 3D head motion and gait trajectory, with super-resolution analysis. The face extraction procedure consists of three stages: i) head pose estimation by a 3D ellipsoidal model; ii) face region extraction by using a 2D or a 3D gait trajectory; and iii) frontal face extraction and reconstruction by estimating head pose and using super-resolution techniques. The head pose is estimated by using a 3D ellipsoidal model and non-linear optimisation. Region- and distance-based feature refinement methods are used and a direct mapping from the 2D image coordinate to the object coordinate is developed. In face region extraction the potential face region is extracted based on the 2D gait trajectory model when a person walks towards a camera. We model a looming field and show how this field affects the image sequences of the human walking. By fitting a 2D gait trajectory model the face region can then be tracked. For the general case of the human walking a 3D gait trajectory model and heel strike positions are used to extract the face region in 3D space. Wavelet decomposition is used to detect the gait cycle and a new heel strike detection method is developed. In face extraction a high resolution frontal face image is reconstructed with low resolution face images by analysing super-resolution. Based on the head pose and 3D ellipsoidal model the invalid low resolution face images are filtered and the frontal view face is reconstructed. By adapting the existing super-resolution the high resolution frontal face image can be synthesised, which is demonstrated to be suitable for face recognition. The contributions of this research include the construction of a 3D model for pose estimation from planar imagery and the first use of gait information to enhance the face extraction and recognition process allowing for deployment in surveillance scenarios.

Text
PhdTS_SungUkJung.pdf - Other
Download (6MB)

More information

Published date: May 2012
Organisations: University of Southampton, Southampton Wireless Group

Identifiers

Local EPrints ID: 340358
URI: http://eprints.soton.ac.uk/id/eprint/340358
PURE UUID: 3246f08d-e595-4d0b-ab28-e50f0ed6b2cf
ORCID for Mark S. Nixon: ORCID iD orcid.org/0000-0002-9174-5934

Catalogue record

Date deposited: 13 Aug 2012 10:43
Last modified: 15 Mar 2024 02:35

Export record

Contributors

Author: Sung Uk Jung
Thesis advisor: Mark S. Nixon ORCID iD

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×