Fingerprint Classification through Standard and Weighted Extreme Learning Machines
Abstract
:1. Introduction
 (i)
 As fingerprint classification system, we propose an ELM model based on feature descriptors with the highest performance for fingerprint identification. The introduction of the ELM algorithm is due to its training stage consumes short time, which allows to increase the identification in large fingerprint databases.
 (ii)
 In the weighted ELM, original and decay weighting schemes are developed to improve the classification capability of the classifier by considering complex data distribution, such as fingerprint classes.
 (iii)
 The hyperparameters of the ELMs (regularization and decay parameters, and the number of hidden nodes) are numerically optimized in terms of the geometric mean since this metric normalizes the classification accuracy of each class.
 (iv)
 The combination of the Hong08 feature extractor and the weighted ELM with the presence of the goldenratio in the weighted matrix is superior to the rest of combinations of feature extractors and ELMs, and almost matches the CNNbased methods in terms of accuracy and penetration rate. Nevertheless, our approach has the benefit of a fast learning speed by using any commercial computer.
2. Related Works
 (i)
 Via feature extractors that obtain the most important characteristics of the fingerprint image, by reducing the original size severally. In this context, the feature extractor models with the bestreported results in the literature are [3,5]: Capelli02 [22], Hong08 [23], and Liu10 [24], which are based on global level characteristics of the image such as orientation maps, ridge structure, and singular points, respectively. Afterward, the classification problem is performed trough a supervised learning technique, e.g., support vector machines, or artificial neural networks based on the gradient operation.
 (ii)
 By employing only a CNN directly on the input images, where the feature extractors are discarded. In practice, CNNs are complex networks that combine different types of neuron layers (convolutional, pooling, and fully connected) with diverse activation functions (e.g., Rectified linear unit (RELU), softmax, RELU plus dropout). Besides, it can be accompanied by a Bayesian framework. However, CNNbased approaches require very timeconsuming training process with millions of parameters to be optimized.
3. Background
3.1. Feature Extractors
 (i)
 Capelli02 [22] is based on the orientation map of the fingerprint. The approach registers the core point by using the Poincare method [45]. Then, the fingerprint is represented by a vector of five positions, which is computed by applying a set of dynamic masks directly derived from each class. The feature vector also stores the orientations.
 (ii)
 (iii)
 Liu10 [24] represents the fingerprint by building a feature vector based on the relative measures among the singular points. Singular points are detected by computing complex filter responses at multiple scales [47]. Thus, the feature vector consists of the relative position, direction, and certainties of each singular point for each scale.
3.2. Extreme Learning Machines
Algorithm 1 ELM learning procedure. 
Given the training set $\Omega =\{({x}_{i},{t}_{i})\mid i=1,..,L\}$, activation function $g(\xb7)$, regularization parameter C, and hidden neuron number N. 

4. Methodology
4.1. Fingerprint Database
4.2. Results Evaluation by the FiveFold CrossValidation Scheme
4.3. Performance Metrics
5. Results and Discussion
5.1. Estimation of Optimal HyperParameters of the ELMs
5.2. Evaluation and Comparison by Using Classical Metrics: Accuracy and Penetration Rate
5.3. Complexity Analysis
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
CNN  Convolutional neural network 
ELM  Extreme learning machine 
SLFN  Single hidden layer feedforward neural network 
NIST  National institute of standard and technologies 
FVC  Fingerprint verification competition 
OM  Orientation map 
SFINGE  Synthetic fingerprint generator 
RELU  Rectified linear unit 
WELM1  Weighted ELM1 
WELM2  Weighted ELM2 
DWELM  Decay weighted 
Gmean  Geometric mean 
Acc  Root mean square error 
PR  Absolute error of the penetration rate 
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Authors  Year  Feature Extractor  Classifier  Database  Accuracy (%)  Evaluation Time (s) 

Tehseen et al. [1]  2019  None  Bayesian deep CNNs  NISTDB4 (3300 samples) and FVC2002 (1600 samples)  96.1 and 95.5  4393 and 3801 
ElHanmdi et al. [29]  2019  Conic Radon transform (image functions are integrated over conic sections)  CNNs (4 convolutional layers with 3 maxpooling layers followed by a fullyconnected layer)  NISTDB4 (3300 samples)  95.0  0.06 
Saeed et al. [21]  2018  Orientation field with histograms of oriented gradients  Basic ELM with the radial activation function  FVC2004 (3520 samples)  98.7  Not reported 
Peralta et al. [3]  2018  None  CNNs (a new network and a modification of the CaffeNet CNN [30]) with softmax probabilities for the last layer  NISTDB4 (3300 samples) and SFINGE (120,000 samples)  93.73 and 94.58  960 and 2,306 
Shrein [4]  2017  Normalized orientation angles  CNNs with various convolutional, maxpooling, and fully connected layers  NISTDB4 (3300 samples)  95.4  Not reported 
Ge et al. [14]  2017  None  Deep CNNs with 6 diverse layers  NISTDB4 (3,300 samples)  97.9  Not reported 
Michelsanti et al. [13]  2017  None  Pretrained CNNs known as VGGF and VGGS [31]  NISTDB4 (3300 samples)  94.4 and 95.95  32,600 and 108,000 
Alias et al. [32]  2016  Minutiae extraction  Support vector machines  FVC2000 and FVC2002 (each has 880 samples)  92.3 and 92.8  Not reported 
Wang et al. [33]  2016  Orientation field based on a support vector machine  Deep CNNs with 3 complex hidden layers  NISTDB4 (3300 samples)  98.4  Not reported 
Gupta et al. [34]  2015  A combination of the orientation field, directional filtering, and Poincare index  Support vector machines  FVC2004 (1600 samples)  97.9  2.6 for input features 
Galar et al. [5]  2015  Singular points, ridge structure, and filter response  Support vector machines  NISTDB4 (3300 samples) and SFINGE (30,000 samples)  92.6 and 95.7  Not reported 
Dorasamy et al. [35]  2015  Directional patters and singular points  Decision tree  FVC2002 and FVC2004 (each has 880 samples)  91.54 and 93.2  Not reported 
Jung et al. [36]  2015  Ridges based on a block of 16 × 16 pixels  Regional local models using conditional probabilities  FVC 2000, 2002, and 2004 (each has 10,304 samples)  97.4  Not reported 
Vitello et al. [37]  2014  Fuzzy Cmeans based on centroids  Naive Bayes  NISTDB4 (3300 samples) and FVC2002 (3200 samples)  91.74 and 80.1  Not reported 
Galar et al. [38]  2014  FingerCode and/or singular points (cores and deltas)  Fuzzy rule learning based on linguistic terms  SFINGE (30,000 samples)  93.78  Not reported 
Guo et al. [11]  2014  Singular points and orientation field  Decision tree  FVC 2000, 2002, and 2004 (7,920 samples in total)  92.74  Out of context 
Luo et al. [39]  2014  Curvelet transform together with graylevel coocurrence matrices  Knearest neighbors  NISTDB4 (3300 samples)  94.6  1.47 
Saini et al. [40]  2013  Hu moments based Wavelet designing  Probabilistic neural network along with support vector machines  FVC 2004 (880 samples)  98.24  Not reported 
Cao et al. [41]  2013  Orientation image, complex filter responses, and ridge line flows  Hierarchic network with five stages (heuristic rules, Knearest neighbor, and support vector machines)  NISTDB4 (3300 samples)  95.9  4.31 
Liu [24]  2010  Multiscale singularities via complex filters  Addaboosted decision trees (combination of weak classifiers)  NISTDB4 (3300 samples)  94.1  1.6 
Rajanna et al. [42]  2010  Orientation map and orientation collinearity  Rank level fusion with Knearest neighbors  NISTDB4 (3300 samples)  91.8  Out of context 
(a) Standard ELM  Capelli02  Hong08  Liu10  

N  C  GMean  N  C  GMean  N  C  GMean  
HQNoPert  3000  ${2}^{10}$  0.64  3000  ${2}^{10}$  0.86  5000  ${2}^{8}$  0.65 
Default  0.54  0.80  0.62  
VQAndPert  0.31  0.58  0.40  
(b) WELM1  Capelli02  Hong08  Liu10  
N  C  GMean  N  C  GMean  N  C  GMean  
HQNoPert  4000  ${2}^{6}$  0.64  5000  ${2}^{4}$  0.92  5000  ${2}^{15}$  0.67 
Default  0.54  0.88  0.63  
VQAndPert  0.37  0.65  0.49  
(c) WELM2  Capelli02  Hong08  Liu10  
N  C  GMean  N  C  GMean  N  C  GMean  
HQNoPert  4000  ${2}^{6}$  0.66  5000  ${2}^{4}$  0.93  5000  ${2}^{15}$  0.69 
Default  0.57  0.89  0.64  
VQAndPert  0.40  0.67  0.51 
DWELM  Capelli02  Hong08  Liu10  

d  GMean  d  GMean  d  GMean  
HQNoPert  9  0.67  16  0.93  15  0.69 
Default  11  0.58  15  0.89  4  0.65 
VQAndPert  10  0.40  20  0.71  12  0.50 
(a) Capelli 02  ELM  WELM1  WELM2  DWELM  

Acc  PR  Acc  PR  Acc  PR  Acc  PR  
HQNoPert  0.80  0.1788  0.79  0.1650  0.81  0.1500  0.79  0.1645 
Default  0.79  0.2112  0.74  0.1969  0.76  0.1785  0.64  0.1942 
VQAndPert  0.61  0.2913  0.60  0.2522  0.63  0.2349  0.61  0.2521 
(b) Hong08  ELM  WELM1  WELM2  DWELM  
Acc  PR  Acc  PR  Acc  PR  Acc  PR  
HQNoPert  0.95  0.0485  0.94  0.0340  0.95  0.0332  0.95  0.0330 
Default  0.94  0.0662  0.93  0.0412  0.94  0.0406  0.94  0.0412 
VQAndPert  0.86  0.0954  0.88  0.0519  0.88  0.0512  0.88  0.0521 
(c) Liu10  ELM  WELM1  WELM2  DWELM  
Acc  PR  Acc  PR  Acc  PR  Acc  PR  
HQNoPert  0.78  0.2060  0.79  0.1727  0.80  0.1651  0.79  0.1711 
Default  0.79  0.2220  0.77  0.1866  0.77  0.1751  0.78  0.1787 
VQAndPert  0.66  0.2696  0.67  0.2327  0.68  0.2166  0.68  0.2315 
Hong08 and WELM2  Modified CaffeNet CNN  New CNN  

Acc  PR  Acc  PR  Acc  PR  
HQNoPert  0.94  0.0332  0.99  0.0051  0.99  0.0031 
Default  0.93  0.0406  0.97  0.0211  0.98  0.0153 
VQAndPert  0.88  0.0512  0.96  0.0329  0.96  0.0279 
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ZabalaBlanco, D.; Mora, M.; Barrientos, R.J.; HernándezGarcía, R.; NaranjoTorres, J. Fingerprint Classification through Standard and Weighted Extreme Learning Machines. Appl. Sci. 2020, 10, 4125. https://doi.org/10.3390/app10124125
ZabalaBlanco D, Mora M, Barrientos RJ, HernándezGarcía R, NaranjoTorres J. Fingerprint Classification through Standard and Weighted Extreme Learning Machines. Applied Sciences. 2020; 10(12):4125. https://doi.org/10.3390/app10124125
Chicago/Turabian StyleZabalaBlanco, David, Marco Mora, Ricardo J. Barrientos, Ruber HernándezGarcía, and José NaranjoTorres. 2020. "Fingerprint Classification through Standard and Weighted Extreme Learning Machines" Applied Sciences 10, no. 12: 4125. https://doi.org/10.3390/app10124125