Background: Fingerprint is an important biological feature of human body, which contains abundant biometric information.At present, the academic exploration of fingerprint gender characteristics is generally at the level of understanding, and the standardization research is quite limited.
Methods: A robust approach is presented in this article, Dense Dilated Convolution ResNet Autoencoder, 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.
Results: The results were explored from three aspects: 1) The efficiency of the DDC-ResNet. 6 typical automatic feature extraction methods with 9 mainstream classifiers for a total of 54 combinations are evaluated on our dataset and provide unbiased experimental 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.
Conclusions: The results are demonstrated that autoencoder networks are a powerful method for extracting gender-specific features to help hide the privacy information of the user’s gender contained in the fingerprint. Our experiments also identified three levels of features in fingerprints that are important for gender differentiation, including loops and whorls shape, bifurcations shape, and line shapes.