Local Deep Neural Networks for gender recognition

By Jordi Mansanet, Alberto Albiol, Roberto Paredes Palacios. 2016

128 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Thomas Lansdall-Welfare
2. Mahmoud Afifi
3. Nello Cristianini
4. Sen Jia
5. Abdelrahman Abdelhamed
6. Os Keyes
7. Francis R. Bach
8. Alessandro Moschitti

Citations in the corpus

• Gender Classification by Deep Learning on Millions of Weakly Labelled Images (2016, methodology): “Test set G16 [5] [18] [15] Unaligned LFW 98.”
• The Misgendering Machines (2018, background): “…triggering an alert should someone of the "wrong" gender enter a space[37], or gendered advertising and user interfaces, in which adverts or applications could (upon detecting a particular person’s gender) alter their presentation to be more appealling to stereotypical members of that gender [55].”
• AFIF4: Deep Gender Classification based on AdaBoost-based Fusion of Isolated Facial Features and Foggy Faces (2017, methodology): “[19] where they utilized Sobel filter in order to extract the local patches while taking into account the location of each patch.”