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IMI/Publicaţii/CSJM/Ediţii/CSJM v.32, n.2 (95), 2024/

Deep Learning Method for Multi-Attribute Analysis of Fingerprint Images

Authors: Diptadip Maiti, Madhuchhanda Basak, Debashis Das
Keywords: Biometric, Fingerprint, CNN, Gender Estimation, Hand Estimation, Finger Estimation.

Abstract

Estimation of gender, hand, and finger to minimize the probable suspects list in a fingerprint database search is a very important stride in forensic anthropology. Previous research attempted to estimate the gender, hand, and finger from the fingerprint, but the results were not consistent. In this effort, we proposed gender, hand, and finger estimation based on fingerprints using a deep convolution neural network. The publicly available SOCOFIG dataset which embraces 55222 no of fingerprints, is used for training and evaluation of the proposed procedure. On the aforementioned dataset, the suggested mode of operation achieves 99.38\% gender, 99.46\% hand, and 97.36\% finger prediction validation accuracy. The results are competitive and commendable when compared to the preceding techniques.

Diptadip Maiti
ORCID: https://orcid.org/0000-0002-0448-7229
Techno India University,
Saltlake, Kolkata, West Bengal, India-700091
E-mail:

Madhuchhanda Basak
ORCID: https://orcid.org/0009-0004-9122-1673
Brainware University, Barasat, Kolkata, West Bengal-700125
E-mail:

Debashis Das
ORCID: https://orcid.org/0000-0002-8456-6006
Techno India University,
Saltlake, Kolkata, West Bengal, India-700091
E-mail:
222

DOI

https://doi.org/10.56415/csjm.v32.11

Fulltext

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