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Regression-based methods for face alignment: A survey
Univ Zagreb, Croatia.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-6763-5487
Univ Zagreb, Croatia.
2021 (English)In: Signal Processing, ISSN 0165-1684, E-ISSN 1872-7557, Vol. 178, article id 107755Article, review/survey (Refereed) Published
Abstract [en]

Face alignment is the process of determining a face shape given its location and size in an image. It is used as a basis for other facial analysis tasks and for human-machine interaction and augmented reality applications. It is a challenging problem due to the extremely high variability in facial appearance affected by many external (illumination, occlusion, head pose) and internal factors (race, facial expression). However, advances in deep learning combined with domain-related knowledge from previous research recently demonstrated impressive results nearly saturating the unconstrained benchmark data sets. The focus is shifting towards reducing the computational burden of the face alignment models since real-time performance is required for such a highly dynamic task. Furthermore, many applications target devices on the edge with limited computational power which puts even greater emphasis on computational efficiency. We present the latest development in regression-based approaches that have led towards nearly solving the face alignment problem in an unconstrained scenario. Various regression architectures are systematically explored and recent training techniques discussed in the context of face alignment. Finally, a benchmark comparison of the most successful methods is presented, taking into account execution time as well, to provide a comprehensive overview of this dynamic research field. (C) 2020 Elsevier B.V. All rights reserved.

Place, publisher, year, edition, pages
ELSEVIER , 2021. Vol. 178, article id 107755
Keywords [en]
Face alignment; Facial feature localization; Facial landmarks detection; Survey; Regression
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-171388DOI: 10.1016/j.sigpro.2020.107755ISI: 000582425800002OAI: oai:DiVA.org:liu-171388DiVA, id: diva2:1501023
Note

Funding Agencies|Visage Technologies AB

Available from: 2020-11-15 Created: 2020-11-15 Last updated: 2021-01-20

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CiteExportLink to record
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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
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  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
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  • asciidoc
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