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

Gender classification aims at recognizing a person's gender. Despite the highaccuracy achieved by state-of-the-art methods for this task, there is stillroom for improvement in generalized and unrestricted datasets. In this paper,we advocate a new strategy inspired by the behavior of humans in genderrecognition. Instead of dealing with the face image as a sole feature, we relyon the combination of isolated facial features and a holistic feature which wecall the foggy face. Then, we use these features to train deep convolutionalneural networks followed by an AdaBoost-based score fusion to infer the finalgender class. We evaluate our method on four challenging datasets todemonstrate its efficacy in achieving better or on-par accuracy withstate-of-the-art methods. In addition, we present a new face dataset thatintensifies the challenges of occluded faces and illumination changes, which webelieve to be a much-needed resource for gender classification research.

AFIF4: Deep Gender Classification based on AdaBoost-based Fusion of Isolated Facial Features and Foggy Faces