Difference between revisions of "Projects:2017s1-100 Face Recognition using 3D Data"
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A dictionary is built for each subject which is made up of that subject's training samples. These dictionaries can be utilised to identify a test sample by exploiting the assumption, that for each subject, these dictionaries will lie on a linear subspace in order to perform classification. | A dictionary is built for each subject which is made up of that subject's training samples. These dictionaries can be utilised to identify a test sample by exploiting the assumption, that for each subject, these dictionaries will lie on a linear subspace in order to perform classification. | ||
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Revision as of 22:57, 28 October 2017
Contents
Introduction
This project seeks to develop a system that is capable of recognising faces captured using commercial off-the-shelf devices. It will be able to capture depth imagery of faces and align them to a common facial pose, before using them to perform recognition. The project will involve elements of literature survey (both sensor hardware and algorithmic techniques), software development (in Matlab), data collection, and performance comparison with existing approaches.
Objectives
Develop a system that is capable of recognizing faces captured using commercial off-the-shelf devices such as the Xbox Kinect.
- Recovery of 3D data from polarimetric imagery
- Recovery of 3D data from Xbox Kinect and alignment to common pose
- Facial recognition from 3D models
Project Team
Jesse Willsmore
Orbille Piol
Michael Sadler
Supervisors
Dr Brian Ng
Dr David Booth (DST Group)
Sau-Yee Yiu (DST Group)
Philip Stephenson (DST Group)
3D data from Polarimetry
3D data from Xbox Kinect and Pre-processing
Facial recognition from 3D models
The proposed method for face recognition utilises sparse representation and is designed to be robust under occlusion and different facial expressions.
Method
A dictionary is built for each subject which is made up of that subject's training samples. These dictionaries can be utilised to identify a test sample by exploiting the assumption, that for each subject, these dictionaries will lie on a linear subspace in order to perform classification.
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