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Open Access Face Alignment via 3D-Assisted Features

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We present a practical 3D-assited face alignment framework based on cascaded regression in this paper. The 3D information embedded in 2D face image is utilized to calculate two novel components to improve the performance of 2D methods in unconstrained face alignment. The two novel components for 2D image features are the projected local patch and the visibility of each landmark. First, we propose to extract the landmark related features in the projected local patches on 2D image from the corresponding 3D face model. Local patches of a fixed landmark in 3D face models for different 2D images cover the same region of face anatomically. The extracted features are more accurate for further locations regression of landmarks. Second, we propose to estimate the visibilities of 2D landmarks based on 3D face model, which are proven to be vital to address large pose face alignment problem. In this paper, we adopt Local Binary Features (LBF) to extract landmark related features in the proposed framework, and name the new method as 3D-Assisted LBF (3DALBF). An extensive evaluation on two face databases shows that 3DALBF can achieve better alignment results than the original 2D method and maintain the speed advantage of 2D method over 3D method.
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Keywords: 3D Morphable Model; 3D-assisted features; Cascaded regression; Face alignment; Facial landmark detection

Document Type: Research Article

Publication date: January 13, 2019

This article was made available online on January 13, 2019 as a Fast Track article with title: "Face alignment via 3D-assisted features".

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  • For more than 30 years, the Electronic Imaging Symposium has been serving those in the broad community - from academia and industry - who work on imaging science and digital technologies. The breadth of the Symposium covers the entire imaging science ecosystem, from capture (sensors, camera) through image processing (image quality, color and appearance) to how we and our surrogate machines see and interpret images. Applications covered include augmented reality, autonomous vehicles, machine vision, data analysis, digital and mobile photography, security, virtual reality, and human vision. IS&T began sole sponsorship of the meeting in 2016. All papers presented at EIs 20+ conferences are open access.

    Please note: For purposes of its Digital Library content, IS&T defines Open Access as papers that will be downloadable in their entirety for free in perpetuity. Copyright restrictions on papers vary; see individual paper for details.

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