Gender classification by deep learning on millions of weakly labelled images

Sen Jia, Thomas Lansdall-Welfare, Nello Cristianini

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

22 Citations (Scopus)
419 Downloads (Pure)

Abstract

When analysing human activities using data mining or machine learning techniques, it can be useful to infer properties such as the gender or age of the people involved. This paper focuses on the sub-problem of gender recognition, which has been studied extensively in the literature, with two main problems remaining unsolved: how to improve the accuracy on real-world face images, and how to generalise the models to perform well on new datasets. We address these problems by collecting five million weakly labelled face images, and performing three different experiments, investigating: The performance difference between convolutional neural networks (CNNs) of differing depths and a support vector machine approach using local binary pattern features on the same training data, the effect of contextual information on classification accuracy, and the ability of convolutional neural networks and large amounts of training data to generalise to cross-database classification. We report record-breaking results on both the Labeled Faces in the Wild (LFW) dataset, achieving an accuracy of 98.90%, and the Images of Groups (GROUPS) dataset, achieving an accuracy of 91.34% for cross-database gender classification.

Original languageEnglish
Title of host publicationProceedings - 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016
PublisherIEEE Computer Society
Pages462-467
Number of pages6
ISBN (Electronic)9781509054725
DOIs
Publication statusPublished - 2 Feb 2017
Event16th IEEE International Conference on Data Mining Workshops, ICDMW 2016 - Barcelona, Spain
Duration: 12 Dec 201615 Dec 2016

Publication series

NameProceedings of the International Conference on Data Mining Workshops
ISSN (Electronic)2375-9259

Conference

Conference16th IEEE International Conference on Data Mining Workshops, ICDMW 2016
CountrySpain
CityBarcelona
Period12/12/1615/12/16

Keywords

  • Gender Classification
  • Deep Learning
  • Big Data

Fingerprint Dive into the research topics of 'Gender classification by deep learning on millions of weakly labelled images'. Together they form a unique fingerprint.

  • Cite this

    Jia, S., Lansdall-Welfare, T., & Cristianini, N. (2017). Gender classification by deep learning on millions of weakly labelled images. In Proceedings - 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016 (pp. 462-467). [7836703] (Proceedings of the International Conference on Data Mining Workshops). IEEE Computer Society. https://doi.org/10.1109/ICDMW.2016.0072