# Fingerprint Classification through Standard and Weighted Extreme Learning Machines
## 1 Introduction
Nevertheless, the prediction accuracy of the ELM algorithm can be susceptible to outlier interference, which is presented for unbalanced datasets [19], such as a fingerprint database. Namely, the basic ELM algorithm trained with an unbalanced dataset may be biased towards the majority class and obtain a superior accuracy on the majority class by affecting minority class accuracy for  
Figure 1: Fingerprint image samples generated, which represent the five fingerprint categories along with their frequency of occurrence in the total population. classification problems. In a fingerprint recognition system, this issue will conduce to some important persons that cannot be found timely. Aiming to face the unbalanced data issue, weighted ELMs were introduced by [PERSON] et al. [10]. This improved ELM incorporates weights to mitigate interference from outliers in the learning procedure. A weighted ELM may automatically adjust the correlation weight of ELM based on the training errors in the training procedure. The weighted ELM shows the best performance on unbalanced datasets contrasted to standard ELM by maintaining the benefits from it (convenient implementation and easy application on multi-class data classification) [20]. Consequently, a weighted ELM is more suitable for fingerprint classification. In our work, standard and weighted ELMs are developed to demonstrate the advantage of the latter for unbalanced datasets. In [21], the problem of fingerprint classification using a standard ELM has been briefly addressed. This study introduces a modified descriptor of the histogram of oriented gradients. The authors of [21] arbitrarily use a radial base activation function, discarding the regularization parameter (over-fitting can occur in the modeling process) for the original ELM, and they do not declare the number of neurons in the hidden layer. Moreover, they do not provide any comparison against other classifiers and use a well-known database (the Fingerprint Verification Competition (FVC) of 2004) only divided into four categories (the arch and tended arch classes are joined as a single class), which can increase the penetration rate and computational cost for the fingerprint identification system. In this paper, we introduce the combination of the better feature extractors and several versions of the ELM for fingerprint classification purposes. The feature extraction step obtains a set of meaningful global features of the fingerprint. On the other hand, the ELM algorithm classifies each fingerprint as one of the five classes of fingerprints. In our study, we consider three feature descriptors (e.g., Capelli02 [22], Hong08 [23], and Liu10 [24]), which will be termed with the name of the first author and the year of publication in the rest of the paper. In addition, three ELM models (e.g., basic ELM [17], original weighted ELM [10] and decay weighted ELM [20]) are developed. The Synthetic Fingerprint Generator (SFINGE) dataset [25; 26] is utilized since its images are naturally distributed into five classes. In addition, this dataset contains fingerprints of different qualities (high, normal, and low), by allowing the simulation of several real-world scenarios. The main novelties and contributions of our study can be summarized as follows:  
1. 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. 2. 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. 3. The hyper-parameters 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. 4.