Inferring user gender from user generated visual content on a deep semantic space

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Abstract

In this paper we address the task of gender classification on picture sharing social media networks such as Instagram and Flickr. We aim to infer the gender of an user given only a small set of the images shared in its profile. We make the assumption that user's images contain a collection of visual elements that implicitly encode discriminative patterns that allow inferring its gender, in a language independent way. This information can then be used in personalisation and recommendation. Our main hypothesis is that semantic visual features are more adequate for discriminating high-level classes. The gender detection task is formalised as: given an user's profile, represented as a bag of images, we want to infer the gender of the user. Social media profiles can be noisy and contain confounding factors, therefore we classify bags of user-profile's images to provide a more robust prediction. Experiments using a dataset from the picture sharing social network Instagram show that the use of multiple images is key to improve detection performance. Moreover, we verify that deep semantic features are more suited for gender detection than low-level image representations. The methods proposed can infer the gender with precision scores higher than 0.825, and the best performing method achieving 0.911 precision.

Original languageEnglish
Title of host publication2018 26th European Signal Processing Conference, EUSIPCO 2018
PublisherIEEE Computer Society
Pages1800-1804
Number of pages5
ISBN (Electronic)9789082797015
DOIs
Publication statusPublished - 29 Nov 2018
Event26th European Signal Processing Conference, EUSIPCO 2018 - Rome, Italy
Duration: 3 Sep 20187 Sep 2018

Publication series

NameEuropean Signal Processing Conference (EUSIPCO)
PublisherIEEE Computer Society
Volume2018-September
ISSN (Print)2076-1465

Conference

Conference26th European Signal Processing Conference, EUSIPCO 2018
CountryItaly
CityRome
Period3/09/187/09/18

Keywords

  • Feature spaces
  • Gender detection
  • Image classification
  • Social media

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