New Robust Part-Based Model with Affine Transformations for Facial Landmark Localization and Detection in Big Data

In this paper, we developed a new robust part-based model for facial landmark localization and detection via affine transformation. In contrast to the existing works, the new algorithm incorporates affine transformations with the robust regression to tackle the potential effects of outliers and heav...

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Autores principales:	Chentao Zhang, Habte Tadesse Likassa, Peidong Liang, Jielong Guo
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/a270d9f7a4a2454ea8d1ffc4e1175b0d
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spelling	oai:doaj.org-article:a270d9f7a4a2454ea8d1ffc4e1175b0d2021-11-15T01:19:17ZNew Robust Part-Based Model with Affine Transformations for Facial Landmark Localization and Detection in Big Data1687-560510.1155/2021/9995074https://doaj.org/article/a270d9f7a4a2454ea8d1ffc4e1175b0d2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/9995074https://doaj.org/toc/1687-5605In this paper, we developed a new robust part-based model for facial landmark localization and detection via affine transformation. In contrast to the existing works, the new algorithm incorporates affine transformations with the robust regression to tackle the potential effects of outliers and heavy sparse noises, occlusions and illuminations. As such, the distorted or misaligned objects can be rectified by affine transformations and the patterns of occlusions and outliers can be explicitly separated from the true underlying objects in big data. Moreover, the search of the optimal parameters and affine transformations is cast as a constrained optimization programming. To mitigate the computations, a new set of equations is derived to update the parameters involved and the affine transformations iteratively in a round-robin manner. Our way to update the parameters compared to the state of the art of the works is relatively better, as we employ a fast alternating direction method for multiplier (ADMM) algorithm that solves the parameters separately. Simulations show that the proposed method outperforms the state-of-the-art works on facial landmark localization and detection on the COFW, HELEN, and LFPW datasets.Chentao ZhangHabte Tadesse LikassaPeidong LiangJielong GuoHindawi LimitedarticleElectronic computers. Computer scienceQA75.5-76.95ENModelling and Simulation in Engineering, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Electronic computers. Computer science QA75.5-76.95
spellingShingle	Electronic computers. Computer science QA75.5-76.95 Chentao Zhang Habte Tadesse Likassa Peidong Liang Jielong Guo New Robust Part-Based Model with Affine Transformations for Facial Landmark Localization and Detection in Big Data
description	In this paper, we developed a new robust part-based model for facial landmark localization and detection via affine transformation. In contrast to the existing works, the new algorithm incorporates affine transformations with the robust regression to tackle the potential effects of outliers and heavy sparse noises, occlusions and illuminations. As such, the distorted or misaligned objects can be rectified by affine transformations and the patterns of occlusions and outliers can be explicitly separated from the true underlying objects in big data. Moreover, the search of the optimal parameters and affine transformations is cast as a constrained optimization programming. To mitigate the computations, a new set of equations is derived to update the parameters involved and the affine transformations iteratively in a round-robin manner. Our way to update the parameters compared to the state of the art of the works is relatively better, as we employ a fast alternating direction method for multiplier (ADMM) algorithm that solves the parameters separately. Simulations show that the proposed method outperforms the state-of-the-art works on facial landmark localization and detection on the COFW, HELEN, and LFPW datasets.
format	article
author	Chentao Zhang Habte Tadesse Likassa Peidong Liang Jielong Guo
author_facet	Chentao Zhang Habte Tadesse Likassa Peidong Liang Jielong Guo
author_sort	Chentao Zhang
title	New Robust Part-Based Model with Affine Transformations for Facial Landmark Localization and Detection in Big Data
title_short	New Robust Part-Based Model with Affine Transformations for Facial Landmark Localization and Detection in Big Data
title_full	New Robust Part-Based Model with Affine Transformations for Facial Landmark Localization and Detection in Big Data
title_fullStr	New Robust Part-Based Model with Affine Transformations for Facial Landmark Localization and Detection in Big Data
title_full_unstemmed	New Robust Part-Based Model with Affine Transformations for Facial Landmark Localization and Detection in Big Data
title_sort	new robust part-based model with affine transformations for facial landmark localization and detection in big data
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/a270d9f7a4a2454ea8d1ffc4e1175b0d
work_keys_str_mv	AT chentaozhang newrobustpartbasedmodelwithaffinetransformationsforfaciallandmarklocalizationanddetectioninbigdata AT habtetadesselikassa newrobustpartbasedmodelwithaffinetransformationsforfaciallandmarklocalizationanddetectioninbigdata AT peidongliang newrobustpartbasedmodelwithaffinetransformationsforfaciallandmarklocalizationanddetectioninbigdata AT jielongguo newrobustpartbasedmodelwithaffinetransformationsforfaciallandmarklocalizationanddetectioninbigdata
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New Robust Part-Based Model with Affine Transformations for Facial Landmark Localization and Detection in Big Data

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