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
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
the 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)