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Fingerprint pattern identification and classification approach based on convolutional neural networks

  • Advances in Parallel and Distributed Computing for Neural Computing
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Abstract

Fingerprint pattern recognition and classification can be of assistance in the research on human personality. In some previous studies, fingerprints were classified into four categories to speed up recognition, but the method of that classification is not suitable for researching the diversity of human personalities. Therefore, in this paper, fingerprint patterns were classified into six types and the accuracy of the recognition was improved to facilitate the research on human personality characteristics. Based on this idea, a six-category fingerprint database is annotated manually and a convolutional neural network (CNN) is proposed for identifying real fingerprint patterns. The new CNN consists of four convolutional layers, three max-pooling layers, two norm layers, and three fully connected layers. The best accuracy the model achieved was 94.87% for a six-category fingerprint database and 92.9% accuracy for a four-category fingerprint database. The results of experimental tests show that the proposed model can recognize the pattern features from a large fingerprint database using the automatic learning and feature extraction abilities of the CNN to get a greater accuracy than in previous experiments.

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Acknowledgements

This research was partially funded by the National Key Research and Development Program of China (Grant No. 2016YFB0201800), Hunan Provincial Key Research and Development Program (Grant No. 2018GK2055).

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Authors and Affiliations

  1. School of Information Science and Engineering, Hunan University, Changsha, 410082, China

    Fan Wu, Juelin Zhu & Xiaomeng Guo

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  1. Fan Wu
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  2. Juelin Zhu
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  3. Xiaomeng Guo
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Correspondence to Fan Wu.

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Wu, F., Zhu, J. & Guo, X. Fingerprint pattern identification and classification approach based on convolutional neural networks. Neural Comput & Applic 32, 5725–5734 (2020). https://doi.org/10.1007/s00521-019-04499-w

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