Publication Date: 2023/03/03
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,)
DOI: https://doi.org/10.5281/zenodo.7696195
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT23FEB1204.pdf
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