Fingerprint pattern identification and classification approach based on convolutional neural networks

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 hu...

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Veröffentlicht in:Neural computing & applications Jg. 32; H. 10; S. 5725 - 5734
Hauptverfasser: Wu, Fan, Zhu, Juelin, Guo, Xiaomeng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Springer London 01.05.2020
Springer Nature B.V
Schlagworte:
Accuracy
Advances in Parallel and Distributed Computing for Neural Computing
Artificial Intelligence
Artificial neural networks
Biometric recognition systems
Classification
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Feature extraction
Feature recognition
Fingerprinting
Fingerprints
Image Processing and Computer Vision
Model accuracy
Neural networks
Pattern recognition
Personality
Probability and Statistics in Computer Science
Pattern feature
Fingerprint
Identification
Convolutional neural network
ISSN:0941-0643, 1433-3058
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-019-04499-w