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

Real-world face detection and alignment demand an advanced discriminativemodel to address challenges by pose, lighting and expression. Illuminated bythe deep learning algorithm, some convolutional neural networks based facedetection and alignment methods have been proposed. Recent studies haveutilized the relation between face detection and alignment to make modelscomputationally efficiency, however they ignore the connection between eachcascade CNNs. In this paper, we propose an structure to propose higher qualitytraining data for End-to-End cascade network training, which give computersmore space to automatic adjust weight parameter and accelerate convergence.Experiments demonstrate considerable improvement over existing detection andalignment models.

Improved Face Detection and Alignment using Cascade Deep Convolutional Network