Data Augmentation by Using Object Shape Reconstruction
(2017) In Master's Theses in Mathematical Sciences FMA820 20171Mathematics (Faculty of Engineering)
- Abstract
- Achieving high performance face recognition often requires large manually labeled training datasets. As such datasets can be difficult to obtain, we investigate whether smaller datasets can be augmented synthetically in order to increase performance.
We use 3D morphable models to create 3D reconstructions of faces from only a single image. The 3D reconstructions are used to render new face images in different poses in order to augment the original dataset. We also investigate whether generative adversarial networks (GANs) can be used to create completely synthetic training datasets for face recognition.
We show that recognition performance can be improved for non-frontal images when augmenting with similarly posed synthetic images.... (More) - Achieving high performance face recognition often requires large manually labeled training datasets. As such datasets can be difficult to obtain, we investigate whether smaller datasets can be augmented synthetically in order to increase performance.
We use 3D morphable models to create 3D reconstructions of faces from only a single image. The 3D reconstructions are used to render new face images in different poses in order to augment the original dataset. We also investigate whether generative adversarial networks (GANs) can be used to create completely synthetic training datasets for face recognition.
We show that recognition performance can be improved for non-frontal images when augmenting with similarly posed synthetic images. Quality over quantity is found to be one of the most important aspects of the synthesizing procedure, where few high quality synthetic images perform better than many low quality synthetic images. We conclude that if higher quality reconstructions are achieved, the performance could be further improved. For future work, GANs seem promising for the task at hand. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8914822
- author
- Granholm, Mats LU and Johannesson, Filip LU
- supervisor
- organization
- course
- FMA820 20171
- year
- 2017
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- Morphable models, Generative adversarial networks, Data augmentation
- publication/series
- Master's Theses in Mathematical Sciences
- report number
- LUTFMA-3326-2017
- ISSN
- 1404-6342
- other publication id
- 2017:E41
- language
- English
- id
- 8914822
- date added to LUP
- 2017-06-20 15:09:05
- date last changed
- 2017-06-20 15:09:05
@misc{8914822,
abstract = {{Achieving high performance face recognition often requires large manually labeled training datasets. As such datasets can be difficult to obtain, we investigate whether smaller datasets can be augmented synthetically in order to increase performance.
We use 3D morphable models to create 3D reconstructions of faces from only a single image. The 3D reconstructions are used to render new face images in different poses in order to augment the original dataset. We also investigate whether generative adversarial networks (GANs) can be used to create completely synthetic training datasets for face recognition.
We show that recognition performance can be improved for non-frontal images when augmenting with similarly posed synthetic images. Quality over quantity is found to be one of the most important aspects of the synthesizing procedure, where few high quality synthetic images perform better than many low quality synthetic images. We conclude that if higher quality reconstructions are achieved, the performance could be further improved. For future work, GANs seem promising for the task at hand.}},
author = {{Granholm, Mats and Johannesson, Filip}},
issn = {{1404-6342}},
language = {{eng}},
note = {{Student Paper}},
series = {{Master's Theses in Mathematical Sciences}},
title = {{Data Augmentation by Using Object Shape Reconstruction}},
year = {{2017}},
}