Regression-based methods for face alignment: A survey

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Gogic, IvanUniv Zagreb, Croatia (author)

Regression-based methods for face alignment: A survey

Article/chapterEnglish2021

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ELSEVIER,2021
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LIBRIS-ID:oai:DiVA.org:liu-171388
https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-171388URI
https://doi.org/10.1016/j.sigpro.2020.107755DOI

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Language:English
Summary in:English

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Funding Agencies|Visage Technologies AB
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.

Subject headings and genre

NATURVETENSKAP Data- och informationsvetenskap Datorseende och robotik hsv//swe
NATURAL SCIENCES Computer and Information Sciences Computer Vision and Robotics hsv//eng
Face alignment; Facial feature localization; Facial landmarks detection; Survey; Regression

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Ahlberg, JörgenLinköpings universitet,Datorseende,Tekniska fakulteten(Swepub:liu)jorah44 (author)
Pandzic, Igor S.Univ Zagreb, Croatia (author)
Univ Zagreb, CroatiaDatorseende (creator_code:org_t)

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In:Signal Processing: ELSEVIER1780165-16841872-7557

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By the author/editor: Gogic, Ivan; Ahlberg, Jörgen; Pandzic, Igor S.

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NATURAL SCIENCES: NATURAL SCIENCES; and Computer and Inf ...; and Computer Vision ...

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By the university: Linköping University

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