# Fingerprint Classification through Standard and Weighted Extreme Learning Machines
## 6 Conclusions  
In this work, we have carried out an extensive study to address fingerprint classification problems by introducing basic and weighted ELMs as classifiers for the first time. Regarding this purpose, we have considered fingerprint databases of high, normal, and low qualities, and three feature extraction methods, which have been reported in the literature as the top performers. The weighted ELMs \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Hong08+WELM2** & **Improved CaffeNet CNN [3; 30]** & **Novel CNN [3]** \\ \hline HQNoPert & 880 & 2306 & 960 \\ Default & 885 & 2329 & 957 \\ VQAndPert & 882 & 2328 & 960 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison in terms of the model learning time expressed in seconds. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Hong08 and W-ELM2**} & \multicolumn{2}{c}{**Modified CaffeNet CNN**} & \multicolumn{2}{c}{**New CNN**} \\ \cline{2-7}  & **Acc** & **PR** & **Acc** & **PR** & **Acc** & **PR** \\ \hline 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 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the results achieved by the best combination (feature extractor and type of ELM), the modified CaffeNET CNN [3; 30], and the CNN model proposed in [3]. are able to deal with data with unbalanced class distribution, such as fingerprint databases. Three weighting schemes are tested in terms of the geometric mean, accuracy, and penetration rate, which demonstrate the better performance of weighted ELM contrasted with standard ELM. All the highlights presented in the study open the possibility of using our introduced classifier for large-scale fingerprint identification systems. Investigations regarding the standard and improved ELMs can be directed towards the introduction of multilayer ELMs for fingerprint recognition systems in order to increase overall effectiveness while maintaining a fast learning speed [59; 60]. As CNNs, multilayer ELMs can ignore the feature extraction stage, i.e., the image processing will be included in the training of the classifier. Comparison results with CNNs in terms of the computational cost are still open questions. To this end, the same hardware and software should be used to establish non-questionable conclusions. For the purpose of emulating real and complex identification problems, finally, the analysis with very-large fingerprint databases (in the order of hundreds of thousands) is proposed as a pending task. Conceptualization, D.Z.-B. and M.M. ; methodology, D.Z.-B. and M.M. ; software, D.Z.-B. and M.M. ; formal analysis, D.Z.-B. and M.M. ; investigation, D.Z.-B. and M.M. ; writing--original draft preparation, D.Z.-B., M.M., and R.H.-G.; writing--review and editing, D.Z.-B., M.M., R.H.-G., R.J.B., and J.N.-T.; project administration, D.Z.-B. and M.M. ; funding acquisition, D.Z.-B., M.M., and R.J.B. All authors have read and agreed to the published version of the manuscript. This research was funded by FONDECYT REGULAR 2020 N\({}^{\circ}\) 1200810 Very Large Fingerprint Classification based on a Fast and Distributed Extreme Learning Machine, Agencia Nacional de Investigacion y Desarrollo, Ministerio de Ciencia, Tecnologia, Conocimiento e Innovacion, Gobierno de Chile, and Project CONICYT FONDEF/ Cuarto Concurso IDeA en dos Etapas del Fondo de Fomento al Desarrollo Cientifico y Tecnologico, Programa IDeA, FONDEF/CONICYT 2017 ID1710254. The authors of the paper thank Dr. [PERSON], international collaborator of the FONDECYT REGULAR 2020 N\({}^{\circ}\) 1200810 and FONDEF 2017 ID1710254 projects. We consider that his contribution allowed to theoretically understand the fingerprint classification and fingerprint recognition problems.