Handwriting-based gender classification using end-to-end deep neural networks

Evyatar Illouz, Eli Omid David, Nathan S. Netanyahu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

25 Scopus citations

Abstract

Handwriting-based gender classification is a well-researched problem that has been approached mainly by traditional machine learning techniques. In this paper, we propose a novel deep learning-based approach for this task. Specifically, we present a convolutional neural network (CNN), which performs automatic feature extraction from a given handwritten image, followed by classification of the writer’s gender. Also, we introduce a new dataset of labeled handwritten samples, in Hebrew and English, of 405 participants. Comparing the gender classification accuracy on this dataset against human examiners, our results show that the proposed deep learning-based approach is substantially more accurate than that of humans.

Original languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning – ICANN 2018 - 27th International Conference on Artificial Neural Networks, 2018, Proceedings
EditorsYannis Manolopoulos, Barbara Hammer, Vera Kurkova, Lazaros Iliadis, Ilias Maglogiannis
PublisherSpringer Verlag
Pages613-621
Number of pages9
ISBN (Print)9783030014230
DOIs
StatePublished - 2018
Event27th International Conference on Artificial Neural Networks, ICANN 2018 - Rhodes, Greece
Duration: 4 Oct 20187 Oct 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11141 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference27th International Conference on Artificial Neural Networks, ICANN 2018
Country/TerritoryGreece
CityRhodes
Period4/10/187/10/18

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2018.

Keywords

  • Convolutional neural network
  • Deep neural network
  • Gender classification
  • HEBIU handwriting dataset
  • Offline handwriting

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