Research on Gender-related Fingerprint Features
- URL: http://arxiv.org/abs/2108.08233v1
- Date: Wed, 18 Aug 2021 16:54:34 GMT
- Title: Research on Gender-related Fingerprint Features
- Authors: Yong Qi, Yanping Li, Huawei Lin, Jiashu Chen, Huaiguang Lei
- Abstract summary: We propose a more robust method, Dense Dilated Convolution ResNet (DDC-ResNet) to extract valid gender information from fingerprints.
By replacing the normal convolution operations with the atrous convolution in the backbone, prior knowledge is provided to keep the edge details and the global reception field can be extended.
Experimental results demonstrate that the combination of our approach outperforms other combinations in terms of average accuracy and separate-gender accuracy.
- Score: 3.0466371774923644
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Fingerprint is an important biological feature of human body, which contains
abundant gender information. At present, the academic research of fingerprint
gender characteristics is generally at the level of understanding, while the
standardization research is quite limited. In this work, we propose a more
robust method, Dense Dilated Convolution ResNet (DDC-ResNet) to extract valid
gender information from fingerprints. By replacing the normal convolution
operations with the atrous convolution in the backbone, prior knowledge is
provided to keep the edge details and the global reception field can be
extended. We explored the results in 3 ways: 1) The efficiency of the
DDC-ResNet. 6 typical methods of automatic feature extraction coupling with 9
mainstream classifiers are evaluated in our dataset with fair implementation
details. Experimental results demonstrate that the combination of our approach
outperforms other combinations in terms of average accuracy and separate-gender
accuracy. It reaches 96.5% for average and 0.9752 (males)/0.9548 (females) for
separate-gender accuracy. 2) The effect of fingers. It is found that the best
performance of classifying gender with separate fingers is achieved by the
right ring finger. 3) The effect of specific features. Based on the
observations of the concentrations of fingerprints visualized by our approach,
it can be inferred that loops and whorls (level 1), bifurcations (level 2), as
well as line shapes (level 3) are connected with gender. Finally, we will open
source the dataset that contains 6000 fingerprint images
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