Authors :
Diptadip Maiti; Madhuchhanda Basak
Volume/Issue :
Volume 8 - 2023, Issue 2 - February
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://bit.ly/3ZDxAy8
DOI :
https://doi.org/10.5281/zenodo.7696195
Abstract :
People may be easily distinguished from one
another thanks to their distinctive and special traits, which
also serve as a means of identification. One of the most
important pieces of identification information is gender. If
we can confidently determine a person’s gender, it will
reduce the number of inquiries and shorten the search
period while increasing the likelihood that someone will be
recognized. In this work, we apply deep convolution
Neural Network to classify fingerprints by means of
gender. The proposed model achieves an validation
accuracy of 96.46% for the classification of gender.
Publicly available Sokoto Coventry Fingerprint Dataset
(SOCOFing) is applied as a benchmark for the outcome of
the classification accuracy of the proposed network.
Keywords :
Biometric, Fingerprint, Deep Learning, CNN,Gender Identification,)
People may be easily distinguished from one
another thanks to their distinctive and special traits, which
also serve as a means of identification. One of the most
important pieces of identification information is gender. If
we can confidently determine a person’s gender, it will
reduce the number of inquiries and shorten the search
period while increasing the likelihood that someone will be
recognized. In this work, we apply deep convolution
Neural Network to classify fingerprints by means of
gender. The proposed model achieves an validation
accuracy of 96.46% for the classification of gender.
Publicly available Sokoto Coventry Fingerprint Dataset
(SOCOFing) is applied as a benchmark for the outcome of
the classification accuracy of the proposed network.
Keywords :
Biometric, Fingerprint, Deep Learning, CNN,Gender Identification,)
