A Cross‐Dimension Annotations Method for 3D Structural Facial Landmark Extraction

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dc.contributor.authorGong, Xunen_US
dc.contributor.authorChen, Pingen_US
dc.contributor.authorZhang, Zheminen_US
dc.contributor.authorChen, Keen_US
dc.contributor.authorXiang, Yueen_US
dc.contributor.authorLi, Xinen_US
dc.contributor.editorBenes, Bedrich and Hauser, Helwigen_US
dc.date.accessioned2020-05-22T12:24:47Z
dc.date.available2020-05-22T12:24:47Z
dc.date.issued2020
dc.description.abstractRecent methods for 2D facial landmark localization perform well on close‐to‐frontal faces, but 2D landmarks are insufficient to represent 3D structure of a facial shape. For applications that require better accuracy, such as facial motion capture and 3D shape recovery, 3DA‐2D (2D Projections of 3D Facial Annotations) is preferred. Inferring the 3D structure from a single image is an problem whose accuracy and robustness are not always guaranteed. This paper aims to solve accurate 2D facial landmark localization and the transformation between 2D and 3DA‐2D landmarks. One way to increase the accuracy is to input more precisely annotated facial images. The traditional cascaded regressions cannot effectively handle large or noisy training data sets. In this paper, we propose a Mini‐Batch Cascaded Regressions (MBCR) method that can iteratively train a robust model from a large data set. Benefiting from the incremental learning strategy and a small learning rate, MBCR is robust to noise in training data. We also propose a new Cross‐Dimension Annotations Conversion (CDAC) method to map facial landmarks from 2D to 3DA‐2D coordinates and vice versa. The experimental results showed that CDAC combined with MBCR outperforms the‐state‐of‐the‐art methods in 3DA‐2D facial landmark localization. Moreover, CDAC can run efficiently at up to 110 on a 3.4 GHz‐CPU workstation. Thus, CDAC provides a solution to transform existing 2D alignment methods into 3DA‐2D ones without slowing down the speed. Training and testing code as well as the data set can be downloaded from https://github.com/SWJTU‐3DVision/CDAC.en_US
dc.description.number1
dc.description.sectionheadersArticles
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume39
dc.identifier.doi10.1111/cgf.13895
dc.identifier.issn1467-8659
dc.identifier.pages623-636
dc.identifier.urihttps://doi.org/10.1111/cgf.13895
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf13895
dc.publisher© 2020 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltden_US
dc.subject3DA‐2D
dc.subject3D face
dc.subjectmini‐batch cascade regression
dc.subjectface alignment
dc.subjectComputing methodologies → Artificial intelligence → Computer vision → Computer vision problems → Interest point and salient region detections
dc.titleA Cross‐Dimension Annotations Method for 3D Structural Facial Landmark Extractionen_US
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