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Extended multi-spectral imaging for gender classification based on image set

Authors:

Narayan Vetrekar,

R. Raghavendra,

Christoph BuschAuthors Info & Claims

SIN '17: Proceedings of the 10th International Conference on Security of Information and Networks

Pages 125 - 130

https://doi.org/10.1145/3136825.3136886

Published: 13 October 2017 Publication History

Abstract

Gender prediction based on facial features has received significant attention in computer vision and biometric community. Most of the gender classification studies mainly focused there attention on approaches that operate in the visible spectrum. In this paper we present gender classification using extended multi-spectral face data captured in nine narrow spectral bands across the visible near infrared spectrum (530nm to 1000nm). Further, we present the proposed method, that learns for this image set the discriminative spectral band features in the affine space and then classifies the features with a Support Vector Machine (SVM) in a robust manner. The extensive experimental results are presented on the reasonable sample size of 78300 spectral band images using our proposed method. The obtained results shows 90.49±3.56% average classification accuracy, indicating the applicability of our proposed method for gender classification.

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Cited By

Cheng HYap KWen B(2023)Reconciliation of statistical and spatial sparsity for robust visual classificationNeurocomputing10.1016/j.neucom.2023.01.084529(140-151)Online publication date: Apr-2023
https://doi.org/10.1016/j.neucom.2023.01.084
Vetrekar NNaik AGad R(2021)Collaborative Representation for Visible to Band Gender Classification Using Multi-spectral Imaging: Extensive Evaluations by Exploring 22 Photometric Normalization MethodsSN Computer Science10.1007/s42979-021-00910-32:6Online publication date: 8-Oct-2021
https://doi.org/10.1007/s42979-021-00910-3
Vetrekar NRamachandra RRaja KVenkatesh SGad RBusch C(2019)Visible to Band Gender Classification: An Extensive Experimental Evaluation Based on Multi-spectral Imaging2019 15th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)10.1109/SITIS.2019.00030(120-127)Online publication date: Nov-2019
https://doi.org/10.1109/SITIS.2019.00030

Index Terms

Extended multi-spectral imaging for gender classification based on image set
1. Security and privacy
  1. Security services
    1. Authentication
      1. Biometrics

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Published In

cover image ACM Other conferences

SIN '17: Proceedings of the 10th International Conference on Security of Information and Networks

October 2017

321 pages

ISBN:9781450353038

DOI:10.1145/3136825

General Chairs:
Rajveer Singh Shekhawat
Manipal University Jaipur, India
,
M. S. Gaur
IIT Jammu, India
,
Atilla Elci
Aksaray University, Turkey
,
Jaideep Vaidya
Rutgers University
,
Oleg Makarevich
Southern Federal University, Russia
,
Ron Poet
University of Glasgow, UK
,
Mehmet Orgun
McQuarie Univ, Australia
,
Vijaypal S. Dhaka
Manipal University Jaipur, India
,
Manoj K. Bohra
Manipal University Jaipur, India
,
Virender Singh
IIT Bombay, India
,
Ludmila Babenko
Southern Federal University, Russia
,
Naghmeh M. Sheykhkanloo
Napier University, UK
,
Behnam Rahnama
Medis Inc., Iran

Copyright © 2017 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 October 2017

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SIN '17

SIN '17: Security of Information and Networks

October 13 - 15, 2017

Jaipur, India

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Overall Acceptance Rate 102 of 289 submissions, 35%

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Cited By

Cheng HYap KWen B(2023)Reconciliation of statistical and spatial sparsity for robust visual classificationNeurocomputing10.1016/j.neucom.2023.01.084529(140-151)Online publication date: Apr-2023
https://doi.org/10.1016/j.neucom.2023.01.084
Vetrekar NNaik AGad R(2021)Collaborative Representation for Visible to Band Gender Classification Using Multi-spectral Imaging: Extensive Evaluations by Exploring 22 Photometric Normalization MethodsSN Computer Science10.1007/s42979-021-00910-32:6Online publication date: 8-Oct-2021
https://doi.org/10.1007/s42979-021-00910-3
Vetrekar NRamachandra RRaja KVenkatesh SGad RBusch C(2019)Visible to Band Gender Classification: An Extensive Experimental Evaluation Based on Multi-spectral Imaging2019 15th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)10.1109/SITIS.2019.00030(120-127)Online publication date: Nov-2019
https://doi.org/10.1109/SITIS.2019.00030

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