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Authors: Connah Kendrick 1 ; Kevin Tan 1 ; Kevin Walker 2 and Moi Hoon Yap 1

Affiliations: 1 Manchester Metropolitan University, United Kingdom ; 2 Image Metrics Ltd, United Kingdom

ISBN: 978-989-758-290-5

Keyword(s): Facial Landmarking, Android, Deep Learning.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Color and Texture Analyses ; Computer Vision, Visualization and Computer Graphics ; Entertainment Imaging Applications ; Image and Video Analysis ; Image Formation and Preprocessing ; Image Formation, Acquisition Devices and Sensors ; Image Registration ; Mobile Imaging

Abstract: Many modern mobile applications incorporate face detection and landmarking into their systems, such as Snapchat, beauty filters and camera auto-focusing systems, where they implement regression based machine learning algorithms for accurate face landmark detection, allowing the manipulation of facial appearance. The mobile applications that incorporate machine learning have to overcome issues such as lighting, occlusion, camera quality and false detections. A solution could be provided through the resurgence of deep learning with neural networks, as they are showing significant improvements in accuracy and reliability in comparison to the state-of-the-art machine learning. Here, we demonstrate the process by using trained networks on mobile devices and review its effectiveness. We also compare the effects of employing max-pooling layers, as an efficient method to reduce the required processing power. We compared network with 3 different amounts of max-pooling layer and ported one to t he mobile device, the other two could not be ported due to memory restrictions. We will be releasing all code to build, train and use the model in a mobile application. The results show that despite the limited processing capability of mobile devices, neural networks can be used for difficult challenges while still working in real-time. We show a network running on a mobile device on a live data stream and give a recommendation on the structure of the network. (More)

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Paper citation in several formats:
Kendrick, C.; Tan, K.; Walker, K. and Yap, M. (2018). The Application of Neural Networks for Facial Landmarking on Mobile Devices.In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, ISBN 978-989-758-290-5, pages 189-197. DOI: 10.5220/0006623101890197

@conference{visapp18,
author={Connah Kendrick. and Kevin Tan. and Kevin Walker. and Moi Hoon Yap.},
title={The Application of Neural Networks for Facial Landmarking on Mobile Devices},
booktitle={Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,},
year={2018},
pages={189-197},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006623101890197},
isbn={978-989-758-290-5},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,
TI - The Application of Neural Networks for Facial Landmarking on Mobile Devices
SN - 978-989-758-290-5
AU - Kendrick, C.
AU - Tan, K.
AU - Walker, K.
AU - Yap, M.
PY - 2018
SP - 189
EP - 197
DO - 10.5220/0006623101890197

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