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

NIR-to-VIS face recognition is identifying faces of two different domains byextracting domain-invariant features. However, this is a challenging problemdue to the two different domain characteristics, and the lack of NIR facedataset. In order to reduce domain discrepancy while using the existing facerecognition models, we propose a 'Relation Module' which can simply add-on toany face recognition models. The local features extracted from face imagecontain information of each component of the face. Based on two differentdomain characteristics, to use the relationships between local features is moredomain-invariant than to use it as it is. In addition to these relationships,positional information such as distance from lips to chin or eye to eye, alsoprovides domain-invariant information. In our Relation Module, Relation Layerimplicitly captures relationships, and Coordinates Layer models the positionalinformation. Also, our proposed Triplet loss with conditional margin reducesintra-class variation in training, and resulting in additional performanceimprovements. Different from the general face recognition models, our add-onmodule does not need to pre-train with the large scale dataset. The proposedmodule fine-tuned only with CASIA NIR-VIS 2.0 database. With the proposedmodule, we achieve 14.81% rank-1 accuracy and 15.47% verification rate of 0.1%FAR improvements compare to two baseline models.

NIR-to-VIS Face Recognition via Embedding Relations and Coordinates of the Pairwise Features