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3-D Facial Landmarks Detection for Intelligent Video Systems

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Abstract
Facial landmark detection is a fundamental research topic in computer vision that is widely adopted in many applications. Recently, thanks to the development of convolutional neural networks, this topic has been largely improved. This article proposes facial-landmark detector, which is based on a state-of-the-art architecture for landmark localization called stacked hourglass network, to obtain accurate facial landmark-points. More specifically, this article uses residual networks as the backbone instead of a 7 × 7 convolution layer. Additionally, it modifies the hourglass modules by using the residual-dense blocks in the mainstream for capturing more efficient features and the 1 × 1 convolution layers in the branch streams for reducing the model size and computational time, instead of the original residual blocks. The proposed architecture also enhances the features from modified hourglass modules with finer-resolution features via a lateral connection to generate more accurate results. The proposed network can outperform other state-of-the-art methods on the AFLW2000-3D dataset and the LS3D-W dataset, the largest three-dimensional (3-D face) alignment dataset to date.
Author(s)
황 반 탄Deshuang Huang조강현
Issued Date
2021
Type
Article
Keyword
FaceThree-dimensional displaysDetectorsComputer architectureConvolutionTask analysisComputational modeling
DOI
10.1109/TII.2020.2966513
URI
https://oak.ulsan.ac.kr/handle/2021.oak/9125
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_ieee_primary_8959132&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,3-D%20Facial%20Landmarks%20Detection%20for%20Intelligent%20Video%20Systems&offset=0&pcAvailability=true
Publisher
IEEE Transactions on Industrial Informatics
Location
미국
Language
영어
ISSN
1551-3203
Citation Volume
17
Citation Number
1
Citation Start Page
578
Citation End Page
586
Appears in Collections:
Engineering > IT Convergence
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