English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Modern face alignment methods have become quite accurate at predicting thelocations of facial landmarks, but they do not typically estimate theuncertainty of their predicted locations nor predict whether landmarks arevisible. In this paper, we present a novel framework for jointly predictinglandmark locations, associated uncertainties of these predicted locations, andlandmark visibilities. We model these as mixed random variables and estimatethem using a deep network trained with our proposed Location, Uncertainty, andVisibility Likelihood (LUVLi) loss. In addition, we release an entirely newlabeling of a large face alignment dataset with over 19,000 face images in afull range of head poses. Each face is manually labeled with the ground-truthlocations of 68 landmarks, with the additional information of whether eachlandmark is unoccluded, self-occluded (due to extreme head poses), orexternally occluded. Not only does our joint estimation yield accurateestimates of the uncertainty of predicted landmark locations, but it alsoyields state-of-the-art estimates for the landmark locations themselves onmultiple standard face alignment datasets. Our method's estimates of theuncertainty of predicted landmark locations could be used to automaticallyidentify input images on which face alignment fails, which can be critical fordownstream tasks.

LUVLi Face Alignment: Estimating Landmarks' Location, Uncertainty, and Visibility Likelihood