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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

The increase in security concerns due to technological advancements has ledto the popularity of biometric approaches that utilize physiological orbehavioral characteristics for enhanced recognition. Face recognition systems(FRSs) have become prevalent, but they are still vulnerable to imagemanipulation techniques such as face morphing attacks. This study investigatesthe impact of the alignment settings of input images on deep learning facemorphing detection performance. We analyze the interconnections between theface contour and image context and suggest optimal alignment conditions forface morphing detection.

Impact of Image Context for Single Deep Learning Face Morphing Attack Detection