Efficient deformable 3D face model tracking with limited hardware resources

Face fitting methods align deformable models to faces on images using the information given by the image pixels. However, most algorithms are designed to be used in desktop personal computers (PC), or hardware with significant computational power. These approaches are therefore too demanding for dev...

Full description

Saved in:
Bibliographic Details
Published in:Multimedia tools and applications 2020-05, Vol.79 (17-18), p.12373-12400
Main Authors: Goenetxea, Jon, Unzueta, Luis, Dornaika, Fadi, Otaegui, Oihana
Format: Article
Language:English
Subjects:
Algorithms
Computer Communication Networks
Computer Science
Computer simulation
Data Structures and Information Theory
Deformation
Devices
Embedded systems
Formability
Hardware
Man-machine interfaces
Mobile operating systems
Multimedia Information Systems
Operating systems
Optimization
People with disabilities
Personal computers
Real time
Smartphones
Special Purpose and Application-Based Systems
Three dimensional models
Tracking
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Face fitting methods align deformable models to faces on images using the information given by the image pixels. However, most algorithms are designed to be used in desktop personal computers (PC), or hardware with significant computational power. These approaches are therefore too demanding for devices with limited computational power, like the increasingly used ARM-based devices. Besides the hardware limitations, the particularities of each operating system include additional challenges to the implementation of real-time face tracking solutions. To fill the lack of methods designed for platforms with a limited computational power we present an efficient way to fit 3D human face models to monocular images. This approach estimates the head pose and gesture in a 3D environment based on a full perspective projection, using parametric non-linear optimisation. We compare the performance of this method running it on similar ARM-based devices with different operating systems (Linux, Android, and iOS). In all cases, we have measured both accuracy and performance. The efficiency of the method makes it possible to run it in real-time ( ∽ 30fps) on devices with limited computational power like smartphones and embedded systems. These kind of efficient methods are a vital component for human behaviour analysis applications, like driver monitoring systems and human-machine interfaces for disabled people among others.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-019-08515-y