Gender classification using convolutional neural networks based on fingerprint analysis with in-line digital holography

Pachara Thonglim; Setthanun Thongsuwan; Prathan Buranasiri

doi:10.1117/12.3010005

12 March 2024 Gender classification using convolutional neural networks based on fingerprint analysis with in-line digital holography

Pachara Thonglim, Setthanun Thongsuwan, Prathan Buranasiri

Author Affiliations +

Proceedings Volume 12852, Quantitative Phase Imaging X; 128520Q (2024) https://doi.org/10.1117/12.3010005
Event: SPIE BiOS, 2024, San Francisco, California, United States

Conference Poster

Abstract

Gender classification has found applications in various fields, including criminology, biometrics, and surveillance. Historically, different methods for gender identification have been employed, such as analyzing hand shape, gait, iris, and facial features. Fingerprints, being unique to each individual, are formed based on the control of multiple genes on chromosomes. After the 24th embryonic week, a person's fingerprint pattern remains unchanged throughout their life. Numerous studies have explored the use of fingerprints for various purposes, such as investigating mental characteristics, characteristics of hereditary diseases, and cancer screening. This paper focuses on studying fingerprints for the identification and classification of human gender through fingerprint analysis using in-line digital holography. The deep learning model constructed for this study includes two convolutional layers, pooling layers, and dense layers. It was trained on a biometric fingerprint database containing 6,000 images, achieving an impressive 99% accuracy. The model was then utilized to classify human gender based on fingerprint analysis, and its accuracy was tested using fingerprint images obtained through Inline Digital Holography (IDH) technique, achieving an 83% accuracy. The performance of the proposed system demonstrates that fingerprints contain vital features for effectively discriminating a person's gender.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Pachara Thonglim, Setthanun Thongsuwan, and Prathan Buranasiri "Gender classification using convolutional neural networks based on fingerprint analysis with in-line digital holography", Proc. SPIE 12852, Quantitative Phase Imaging X, 128520Q (12 March 2024); https://doi.org/10.1117/12.3010005

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