# Fingerprint Classification through Standard and Weighted Extreme Learning Machines
## 5 Results and Discussion
### Evaluation and Comparison by Using Classical Metrics: Accuracy and Penetration Rate  
Firstly, Table 4 presents the Acc and PR results for the different databases, feature extractors, and ELM variants. While the accuracy is commonly used for regression and classification problems, the PR is recently adopted in the fingerprint classification context [1; 3]. Apart from the reasons exposed in Section 4.3, both metrics were considered for comparison purposes with the CNNs proposed by [PERSON] et al. [3]. It is observed that the Hong08 feature extractor and W-ELM2 must be positively highlighted. In fact, the combination of these produced again the superior metrics, especially for the PR. Instead, the Capelli02 feature extractor and standard ELM hadve the lowest performances. Among the ELM models, the W-ELM2 is able to enhance the recognition rate of minority class to maximize the G-mean and PR values, as well as to guarantee the proper classification of the majority class, keeping a superior Acc (see the outcomes of Section 5.1 and Table 4). Additionally, it is worth to note that the differences among ELMs in terms of Acc and PR metrics were minimum in contrast to the G-mean metric given a feature extractor. Consequently, the relevance of the G-mean as a performance metric for unbalanced datasets is demonstrated. For comparison purposes, a benchmark work of the state-of-the-art is considered. [PERSON] et al. [3] introduce a novel CNN-based model and, also, exploit a modification of the CaffeNet CNN [30] for the fingerprint classification problem. Fingerprint images without computing an explicit feature extractor were processed by both CNNs. The classification performance in terms of the accuracy and penetration rate was only calculated by considering the NIST and SFINGE databases. This paper implements the five-fold cross-validation scheme, and the reported performance was averaged from the five testing sets, which are the same experimental settings used in our work (refer to Section 4.2) by allowing a proper comparison. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**(a)**} & \multicolumn{2}{c}{**ELM**} & \multicolumn{2}{c}{**W-ELM1**} & \multicolumn{2}{c}{**W-ELM2**} & \multicolumn{2}{c}{**DW-ELM**} \\ \cline{2-7}  & **Acc** & **PR** & **Acc** & **PR** & **Acc** & **PR** & **Acc** & **PR** \\ \hline 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 \\ \hline \multirow{2}{*}{**(b)**} & \multicolumn{2}{c}{**ELM**} & \multicolumn{2}{c}{**W-ELM1**} & \multicolumn{2}{c}{**W-ELM2**} & \multicolumn{2}{c}{**DW-ELM**} \\ \cline{2-7}  & **Acc** & **PR** & **Acc** & **PR** & **Acc** & **PR** & **Acc** & **PR** \\ \hline 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 \\ \hline \multirow{2}{*}{**(c)**} & \multicolumn{2}{c}{**ELM**} & \multicolumn{2}{c}{**W-ELM1**} & \multicolumn{2}{c}{**W-ELM2**} & \multicolumn{2}{c}{**DW-ELM**} \\ \cline{2-7}  & **Acc** & **PR** & **Acc** & **PR** & **Acc** & **PR** & **Acc** & **PR** \\ \hline 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 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy and absolute-error penetration rates in terms of database and feature extractor by adopting the optimal hyper-parameters of the studied ELMs.