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Fingerprint classification system using CNN
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AI
DOI：10.1007/s11042-022-12294-4.png)
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Abstract
En 中文
Most solitary finger impression check and acknowledgment frameworks / methods are based on the minutiae feature points. Feature Extraction is a fundamental advance in solitary finger impression based acknowledgment frameworks. In this paper, a CNN based finger impression affirmation strategy is proposed without preprocessing an image. The framework fuses two phases include extraction and coordinating. Feature elicitation is realized by different filters with different parameter set; matching juncture relates extracted features and creates a corresponding score. Recognition attainment of the preferred system has been tested by utilizingFVC2004 database. The inference is very favoring for implementing a CNN based self-regulating fingerprint recognition system. Our method achieves an overall rate of 99.1% of accurately classified samples.
Keywords:
Minutiae
Fingerprint recognition
Fingerprint classification
Batch normalization
CNN
VGGNet
LeNet5
FVC
Multilayer perceptron (MLP)
Feed forward neural networks (FNN)
Channels
filters and feature maps (FM)
Journal
IF:
3
Papers:
2.0W
Citations:
3.2W
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Cited Papers
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