# Fingerprint Classification Based on Multilayer Extreme Learning Machines
## 2 State of the Art
### CNN and Images  
In previous studies, various techniques have been proposed for fingerprint classification using neural networks, with two primary approaches dominating the field: CNNs for image-based classification and ELMs for descriptor-based classification. Regarding CNN-based methods, the work of [PERSON] [10] stands out, in which a convolutional neural network with softmax functions--a modification of AlexNet--was proposed for fingerprint classification. This approach eliminates the need for an explicit feature extraction process while addressing challenges related to image noise or disturbances in fingerprint imaging. For comparison, tests were conducted using three additional classifiers: SVM, decision trees (DTs), and K-Nearest Neighbors (K-NN). For feature extraction, three descriptors were used: Capelli02 [24], Hong08 [14], and Liu10 [25]. The databases were SFinGe and NIST-4 [3]. A five-fold cross-validation (5-FCV) scheme was applied, where each database was randomly divided into five subsets with identical class distributions, enabling the calculation of average performance across all tests. KEEL software (3.0) (Knowledge Extraction based on Evolutionary Learning) was used to implement the SVM, DT, and K-NN classifiers, while the CNNs were implemented using Caffe. The experiments were conducted on a computer with an Intel Core i7-3820 processor (3.60 GHz) and 27 GB of RAM (Santa Clara, CA, USA). The CNN was run with an Nvidia GeForce GTX TITAN GPU (2688 cores, 6144 MB GDDR5 RAM) (Santa Clara, CA, USA). This study aimed to evaluate classification methods from the perspectives of robustness and efficiency, analyzing both the accuracy and computational performance of different approaches. The proposed network achieved an accuracy of 99.60% for the HQNoPert database, corresponding to only 120 errors out of 30,000 fingerprint test samples. The CNN outperformed all combinations of feature extractors and state-of-the-art classifiers, achieving a penetration rate of 29.79%, compared to 39.99% for the original CaffeNet. Furthermore, the CNN demonstrated competitive computational performance, requiring an average of 960 s, which was significantly faster than the 230 s required, on average, by the combination of feature extraction and classification. [PERSON] [23] sought to improve the efficiency of fingerprint classification by using deep convolutional neural networks (DCNNs) in combination with a Bayesian model to address false positives. This approach, termed Bayesian DCNN (B-DCNN), highlights the advantages of incorporating Bayesian methods over traditional DCNNs. Two fingerprint databases were used in the experimentation: NIST-4 and 1-A (FVC DB1-A) from 2002 and 2004, respectively. The classification was based on the five-class system proposed by [PERSON] [26] (arch, left loop, right loop, tented arch, and whorl). While some researchersexclude these fingerprints from the database, others consider these labels valid. To ensure fair experiments, this study used two versions of the NIST-4 database: the complete database, denoted as NIST-4 (2000), and a version with 350 fingerprints, having multiple deleted labels, referred to as NIST-4 (1650). The results showed accuracies of 96.1% for NIST-4 and 95.3% for the FVC 2002 and 2004 databases. The findings demonstrate that the B-DCNN approach outperforms conventional DCNN models, yielding improvements in accuracy of 0.8% and 1.0% compared to the work presented by [PERSON] et al. [27]. The training times for the National Institute of Standards and Technology (NIST) and Fingerprint Verification Competition (FVC) databases were 4.393 and 3.801 s, respectively. [PERSON] [28] proposed a lightweight CNN architecture focused on the singularity region of interest (ROI). The principal dataset was the NIST-4, which contains 4000 grayscale fingerprint images. To enhance the dataset, four data augmentation techniques were applied--vertical flip, horizontal rotation, small-angle rotation, and Gaussian noise--generating an additional 16,000 images.