The fingerprint comes to be the most popular and utilized biometric for identifying persons owing to its bio-invariant characteristic, precision, as well as easy acquisition. A sub-system of an identification system is the classification stage in order to diminish the penetration rate and computational complexity. Actually, there are many formal investigations regarding techniques by exploiting convolutional neural networks (CNN) together with fingerprint images, which have superior performance metrics at the cost of large training times even employing high-performance computing, which is not feasible in the standard world. In our manuscript, researches about identifying and classifying fingerprint databases by recurring to extreme learning machines (ELM) will be extensively reported and discussed for the first time. The diverse methodologies (ELM plus feature extractors) given by the authors will be studied and contrasted considering performance analysis. Consequently, academic papers with diverse versions of ELMs are developed to observe the pros and cons that they exhibit with each other and to probe how they may help for minimizing the penetration rate of fingerprint databases. In fact, this issue is very relevant because enhancing the penetration rate means shorting search times and computational complexity in fingerprints.