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/

Zendy Plus

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

Zendy Tools

Zendy Open

This paper addresses the challenges of face attribute detection specificallyin the Indian context. While there are numerous face datasets in unconstrainedenvironments, none of them captures emotions in different face orientations.Moreover, there is an under-representation of people of Indian ethnicity inthese datasets since they have been scraped from popular search engines. As aresult, the performance of state-of-the-art techniques can't be evaluated onIndian faces. In this work, we introduce a new dataset, IIITM Face, for thescientific community to address these challenges. Our dataset includes 107participants who exhibit 6 emotions in 3 different face orientations. Each ofthese images is further labelled on attributes like gender, presence ofmoustache, beard or eyeglasses, clothes worn by the subjects and the density oftheir hair. Moreover, the images are captured in high resolution with specificbackground colors which can be easily replaced by cluttered backgrounds tosimulate `in the Wild' behaviour. We demonstrate the same by constructing IIITMFace-SUE. Both IIITM Face and IIITM Face-SUE have been benchmarked across keymulti-label metrics for the research community to compare their results.

IIITM Face: A Database for Facial Attribute Detection in Constrained and Simulated Unconstrained Environments