University of Oulu

Álvarez Casado, C., Bordallo López, M. Real-time face alignment: evaluation methods, training strategies and implementation optimization. J Real-Time Image Proc 18, 2239–2267 (2021). https://doi.org/10.1007/s11554-021-01107-w

Real-time face alignment : evaluation methods, training strategies and implementation optimization

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Author: Álvarez Casado, Constantino1; Bordallo López, Miguel1,2
Organizations: 1Center for Machine Vision and Signal Analysis, University of Oulu, Oulu, Finland
2VTT Technical Research Centre of Finland Ltd., Oulu, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 5.9 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2021121060123
Language: English
Published: Springer Nature, 2021
Publish Date: 2021-12-10
Description:

Abstract

Face alignment is a crucial component in most face analysis systems. It focuses on identifying the location of several keypoints of the human faces in images or videos. Although several methods and models are available to developers in popular computer vision libraries, they still struggle with challenges such as insufficient illumination, extreme head poses, or occlusions, especially when they are constrained by the needs of real-time applications. Throughout this article, we propose a set of training strategies and implementations based on data augmentation, software optimization techniques that help in improving a large variety of models belonging to several real-time algorithms for face alignment. We propose an extended set of evaluation metrics that allow novel evaluations to mitigate the typical problems found in real-time tracking contexts. The experimental results show that the generated models using our proposed techniques are faster, smaller, more accurate, more robust in specific challenging conditions and smoother in tracking systems. In addition, the training strategy shows to be applicable across different types of devices and algorithms, making them versatile in both academic and industrial uses.

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Series: Journal of real-time image processing
ISSN: 1861-8200
ISSN-E: 1861-8219
ISSN-L: 1861-8200
Volume: 18
Issue: 6
Pages: 2239 - 2267
DOI: 10.1007/s11554-021-01107-w
OADOI: https://oadoi.org/10.1007/s11554-021-01107-w
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
Subjects:
Funding: Open access funding provided by University of Oulu including Oulu University Hospital.
Copyright information: © The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
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