# Fingerprint Classification Based on Multilayer Extreme Learning Machines
## 2 State of the Art
### CNN and Images
Thus, the final experimental database comprised 20,000 fingerprint images. In the first experiment, the performance of five popular non-neural network classifiers was compared. The second experiment aimed to explore and identify an optimal lightweight neural network architecture for fingerprint classification. The third experiment compared the proposed CNN architecture with three other recently published neural network architectures. The experimental results demonstrate that the proposed architecture achieved an accuracy of 93%, significantly outperforming non-neural network classifiers, including RF, K()NN, logistic regression (LR), SVM, and radial basis function (RBF). More importantly, when compared to other recently published CNN architectures, the proposed structure achieved similar or even better performance, with a parameter scale ranging from 1 = 12 to 1 = 38, thereby accelerating both training and testing. Moreover, the proposed CNN model, with fewer neurons, exhibited better overfitting suppression and greater noise robustness. [PERSON] [29] presented a study on the performance of pre-trained CNNs--specifically, AlexNet, GoogLeNet, and ResNet--tested on two fingerprint databases: the Hong Kong Polytechnic University (PolyU) and NIST databases. The study also compared these results with other works in the literature to determine which classification approach yielded superior performance in terms of accuracy and efficiency. This research represents the first comprehensive analysis comparing widely used CNN architectures for fingerprint classification across four, five, and eight classes. The experimental results revealed that the tested CNN architectures achieved the best performance on the PolyU dataset, likely due to the larger size and higher quality of the sample. To validate the reliability of the findings, statistical analysis was performed using the McNemar test. Together, the databases used in the study--the PolyU and NIST datasets--comprise a total of 7800 fingerprint images. The three established CNN architectures tested in the study were AlexNet [30], GoogLeNet [31], and ResNet [32]. The primary objective of this study was to determine the approach that leads to the best performance among three CNN architectures when used with different datasets, considering the type of dataset as part of the classification task. A Graphic User Interface (GUI) was provided to display the performance of each CNN in relation to the different datasets. The performance of each network can be easily calculated by: (1) using a different dataset and classification task; (2) uploading an image from any database to classify it, selecting the neural network and the number of classes; and (3) identifying the CNN with the best performance for any given fingerprint image. These features make the GUI both a powerful analysis tool and a simple test implementation. In [33], a new fingerprint classification method based on the Histogram of Oriented Gradients (HOG) descriptor was proposed. This method enhances the calculation of ridge patterns within the HOG descriptor, improving its ability to robustly represent a fingerprint, as the resulting orientation clearly delineates the ridges forming the fingerprint image. An ELM with an RBF kernel is employed as the classifier. Experiments were conducted using the FVC-2004 fingerprint database. The proposed method achieved an average accuracy of 98.7%, outperforming the latest fingerprint classification methods based on CNNs [6].