# Fingerprint Classification through Standard and Weighted Extreme Learning Machines
## 1 Introduction  
Fingerprint is one of the widely used biometric techniques for individuals identification purposes because of its bio-invariant and reliability characteristics. Besides, it provides sufficient and necessary details for the differentiation of people [1]. It should be noted that fingerprint is chosen among other biometric techniques (iris, face, voice, hand, and others) due to its high accuracy and low acquisition cost. Fingerprint recognition has several applications for security goals such as forensic and civil registering, as well as an alternative for user authentication [2]. An automatic fingerprint recognition system requires the matching of an input fingerprint sample with a large number of fingerprint templates registered in the biometric database [3]. Taking intoaccount a massive database for a real-world application, an individuals identification system is excessively expensive in terms of search-time and computational cost [4]. For example, the FBI database contains more than 100 million fingerprints, delaying the identification of a person up to 30 min in the best scenario. Of course, high-performance computing methods are employed to get this time. Fingerprint classification is the most adopted approach to reduce the penetration rate in the database [5]. Fingerprint images have structural characteristics based on the pattern of ridges. Thus, according to the morphology of its ridge structure, fingerprints can be classified into five major categories [6]: arch, tented arch, left loop, right loop, and whorl, which are not evenly distributed. Figure 1 depicts these fingerprint classes and their frequency of occurrence in the overall population. In other words, an unbalanced dataset is naturally experienced in which two classes are under-represented relative to others. The problem of unbalanced data is commonly associated with asymmetric costs of misclassifying elements of the diverse classes [7]. In order to deal with unbalanced data, the following methodologies can be adopted: under-sampling approaches [8], over-sampling techniques [9], and algorithmic methods [10]. The former can lead to loss of majority class information, the second approach may produce distortion of the minority class, and the latter is to set different misclassification costs according to each particular class, which is adopted in our work for simplicity and effectiveness purposes. The fingerprint classification task typically consists of three main processes [11; 12]: (i) pre-processing, to reduce the noises and interference in images; (ii) feature extraction, to represent the image as a vector of characteristics; and (iii) classification procedure. This methodology allows building extremely precise classifiers with an acceptable computational cost [2; 5]. An alternative for classifying fingerprints in a single stage is deep convolutional neural networks (CNNs) [1; 3; 13; 14], which are characterized by millions network parameters involved in the learning phase. These approaches obtain yields close to 100% but with a time-consuming training process, despite using computers with the latest generation parallel software and hardware. Extreme learning machine (ELM) has been proposed as a promising training algorithm for single hidden layer feedforward neural networks [7]. In the ELM algorithm, the weights and biases of the hidden layer are randomly generated. Then, the weights of the output layer may be analytically computed via solving a linear system thanks to the Moore-Penrose pseudoinverse matrix [15; 16]. Previous results, both in regression and classification problems, have shown the low computational complexity of ELM compared to the popular backpropagation-based algorithm and support vector machines, especially for high dimensional and large data applications [7; 15; 17; 18]. Besides, it is highlighted for its fast and stable training process, easy implementation, and accuracy in modeling and prediction.