Gender Classification from Behavioural Biometric Data using Convolutional Neural Network

DOI

10.2991/978-94-6463-196-8_49How to use a DOI?

Keywords

Biometrics; Convolutional Neural Network; Offline Handwritten Signature; Gender Classification

Abstract

Biometric modalities are used to identify the gender of an individual based upon their physiometrics or behaviometric data. Gender plays a crucial role in most applications like banking, security, document authorization, forensics, psychology, human-computer interventions, and many more. Gender classification using handwritten signatures is still considered to be a challenging task due to homogenous variations among male and female handwritten signatures. This paper monologues the gender classification from offline signature images using Convolutional Neural Network features. The results obtained are promising and competitive with state-of-art techniques.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Sathish Kumar
AU  - Shivanand S. Gornale
AU  - Rashmi Siddalingappa
PY  - 2023
DA  - 2023/08/10
TI  - Gender Classification from Behavioural Biometric Data using Convolutional Neural Network
BT  - Proceedings of the First International Conference on Advances in Computer Vision and Artificial Intelligence Technologies (ACVAIT 2022)
PB  - Atlantis Press
SP  - 646
EP  - 659
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-196-8_49
DO  - 10.2991/978-94-6463-196-8_49
ID  - Kumar2023
ER  -