Journal
SYMMETRY-BASEL
Volume 13, Issue 5, Pages -Publisher
MDPI
DOI: 10.3390/sym13050750
Keywords
fingerprint classification; deep learning; convolutional neural networks; fingerprint features
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Biometric classification is crucial in fingerprint characterization, and using Convolutional Neural Networks (CNNs) in deep learning for multi-class fingerprint classification has shown the best performance. The study aims to find the most efficient and precise model through comparisons of various CNN architectures on different fingerprint databases.
Biometric classification plays a key role in fingerprint characterization, especially in the identification process. In fact, reducing the number of comparisons in biometric recognition systems is essential when dealing with large-scale databases. The classification of fingerprints aims to achieve this target by splitting fingerprints into different categories. The general approach of fingerprint classification requires pre-processing techniques that are usually computationally expensive. Deep Learning is emerging as the leading field that has been successfully applied to many areas, such as image processing. This work shows the performance of pre-trained Convolutional Neural Networks (CNNs), tested on two fingerprint databases-namely, PolyU and NIST-and comparisons to other results presented in the literature in order to establish the type of classification that allows us to obtain the best performance in terms of precision and model efficiency, among approaches under examination, namely: AlexNet, GoogLeNet, and ResNet. We present the first study that extensively compares the most used CNN architectures by classifying the fingerprints into four, five, and eight classes. From the experimental results, the best performance was obtained in the classification of the PolyU database by all the tested CNN architectures due to the higher quality of its samples. To confirm the reliability of our study and the results obtained, a statistical analysis based on the McNemar test was performed.
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