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Atypical Facial Landmark Localisation with Stacked Hourglass Networks: A Study on 3D Facial Modelling for Medical Diagnosis

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter (peer-reviewed) › peer-review

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Atypical Facial Landmark Localisation with Stacked Hourglass Networks: A Study on 3D Facial Modelling for Medical Diagnosis. / Storey, Gary; Bouridane, Ahmed; Jiang, Richard et al.
Deep Biometrics. ed. / Richard Jiang; Chang-Tsun Li; Danny Crookes; Weizhi Meng; Christophe Rosenberger. Cham: Springer, 2020. p. 37-49 (Unsupervised and Semi-Supervised Learning ).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter (peer-reviewed) › peer-review

Harvard

Storey, G, Bouridane, A, Jiang, R & Li, C-T 2020, Atypical Facial Landmark Localisation with Stacked Hourglass Networks: A Study on 3D Facial Modelling for Medical Diagnosis. in R Jiang, C-T Li, D Crookes, W Meng & C Rosenberger (eds), Deep Biometrics. Unsupervised and Semi-Supervised Learning , Springer, Cham, pp. 37-49. https://doi.org/10.1007/978-3-030-32583-1_3

APA

Storey, G., Bouridane, A., Jiang, R., & Li, C.-T. (2020). Atypical Facial Landmark Localisation with Stacked Hourglass Networks: A Study on 3D Facial Modelling for Medical Diagnosis. In R. Jiang, C.-T. Li, D. Crookes, W. Meng, & C. Rosenberger (Eds.), Deep Biometrics (pp. 37-49). (Unsupervised and Semi-Supervised Learning ). Springer. https://doi.org/10.1007/978-3-030-32583-1_3

Vancouver

Storey G, Bouridane A, Jiang R, Li CT. Atypical Facial Landmark Localisation with Stacked Hourglass Networks: A Study on 3D Facial Modelling for Medical Diagnosis. In Jiang R, Li CT, Crookes D, Meng W, Rosenberger C, editors, Deep Biometrics. Cham: Springer. 2020. p. 37-49. (Unsupervised and Semi-Supervised Learning ). doi: 10.1007/978-3-030-32583-1_3

Author

Storey, Gary ; Bouridane, Ahmed ; Jiang, Richard et al. / Atypical Facial Landmark Localisation with Stacked Hourglass Networks : A Study on 3D Facial Modelling for Medical Diagnosis. Deep Biometrics. editor / Richard Jiang ; Chang-Tsun Li ; Danny Crookes ; Weizhi Meng ; Christophe Rosenberger. Cham : Springer, 2020. pp. 37-49 (Unsupervised and Semi-Supervised Learning ).

Bibtex

@inbook{7f9da9bcbbfa4132a4979baebaa089ef,
title = "Atypical Facial Landmark Localisation with Stacked Hourglass Networks: A Study on 3D Facial Modelling for Medical Diagnosis",
abstract = "While facial biometrics has been widely used for identification purpose, it has recently been researched as medical biometrics for a range of diseases. In this chapter, we investigate the facial landmark detection for atypical 3D facial modelling in facial palsy cases, while potentially such modelling can assist the medical diagnosis using atypical facial features. In our work, a study of landmarks localisation methods such as stacked hourglass networks is conducted and evaluated to ascertain their accuracy when presented with unseen atypical faces. The evaluation highlights that the state-of-the-art stacked hourglass architecture outperforms other traditional methods.",
keywords = "Face detection and modelling, Deep learning, Convolutional neural network, Stacked hourglass network",
author = "Gary Storey and Ahmed Bouridane and Richard Jiang and Chang-Tsun Li",
year = "2020",
month = jan,
day = "29",
doi = "10.1007/978-3-030-32583-1_3",
language = "English",
isbn = "9783030325824",
series = "Unsupervised and Semi-Supervised Learning ",
publisher = "Springer",
pages = "37--49",
editor = "Richard Jiang and Chang-Tsun Li and Danny Crookes and Weizhi Meng and Christophe Rosenberger",
booktitle = "Deep Biometrics",

}

RIS

TY - CHAP

T1 - Atypical Facial Landmark Localisation with Stacked Hourglass Networks

T2 - A Study on 3D Facial Modelling for Medical Diagnosis

AU - Storey, Gary

AU - Bouridane, Ahmed

AU - Jiang, Richard

AU - Li, Chang-Tsun

PY - 2020/1/29

Y1 - 2020/1/29

N2 - While facial biometrics has been widely used for identification purpose, it has recently been researched as medical biometrics for a range of diseases. In this chapter, we investigate the facial landmark detection for atypical 3D facial modelling in facial palsy cases, while potentially such modelling can assist the medical diagnosis using atypical facial features. In our work, a study of landmarks localisation methods such as stacked hourglass networks is conducted and evaluated to ascertain their accuracy when presented with unseen atypical faces. The evaluation highlights that the state-of-the-art stacked hourglass architecture outperforms other traditional methods.

AB - While facial biometrics has been widely used for identification purpose, it has recently been researched as medical biometrics for a range of diseases. In this chapter, we investigate the facial landmark detection for atypical 3D facial modelling in facial palsy cases, while potentially such modelling can assist the medical diagnosis using atypical facial features. In our work, a study of landmarks localisation methods such as stacked hourglass networks is conducted and evaluated to ascertain their accuracy when presented with unseen atypical faces. The evaluation highlights that the state-of-the-art stacked hourglass architecture outperforms other traditional methods.

KW - Face detection and modelling

KW - Deep learning

KW - Convolutional neural network

KW - Stacked hourglass network

U2 - 10.1007/978-3-030-32583-1_3

DO - 10.1007/978-3-030-32583-1_3

M3 - Chapter (peer-reviewed)

SN - 9783030325824

T3 - Unsupervised and Semi-Supervised Learning

SP - 37

EP - 49

BT - Deep Biometrics

A2 - Jiang, Richard

A2 - Li, Chang-Tsun

A2 - Crookes, Danny

A2 - Meng, Weizhi

A2 - Rosenberger, Christophe

PB - Springer

CY - Cham

ER -