Cross-spectral Gender Classification Using Multi-spectral Face Imaging

Soft biometrics has been gaining significant attention in biometrics literature for usage in recognition system. Gender classification is one such soft biometric traits that has been studied extensively using visible spectrum range and limited studies beyond visible spectrum. In this paper we presen...

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Bibliographic Details
Published in:Journal of physics. Conference series 2021-05, Vol.1921 (1), p.12048
Main Authors: Vetrekar, Narayan, Naik, Aparajita, Gad, R. S.
Format: Article
Language:English
Subjects:
Biometrics
Classification
Evaluation
Gender
Image classification
Physics
Spectra
Spectral bands
Support vector machines
Visible spectrum
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Summary:Soft biometrics has been gaining significant attention in biometrics literature for usage in recognition system. Gender classification is one such soft biometric traits that has been studied extensively using visible spectrum range and limited studies beyond visible spectrum. In this paper we present the cross-spectral gender classification using Multi-spectral imaging. We present this study using the facial images captured in nine narrow spectral bands ranging from 530nm to 1000nm. Further, we present an extensive benchmark evaluation based on state-of-the art linear Support Vector Machine (SVM) classifier for facial discriminative features in the cross spectral data in a robust manner. The extensive evaluation results based on 10 fold cross-validation indicates the highest average classification accuracy of 92.84% for cross-spectral study.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1921/1/012048