This work addresses the problem of biometric recognition of fingerprint liveness to identify and differentiate between real fingerprints and their artificial replicas. The main objective was to identify the features that are crucial for fingerprint liveness recognition and based on these features to propose an efficient classification algorithm. We worked with the LivDet database from 2009, which contains both real and fake fingerprints. This database has been used in a worldwide competition and the results of all implemented algorithms are publicly available for subsequent comparison of success rates. An important part of this work was the preprocessing of the image data, which was crucial for testing the selected features and implementing the algorithms. We analyzed more than 180 different features from which we selected the most relevant ones. We then used the selected features to develop several fingerprint recognition and classification algorithms. Using the selected features, several possible variations of the algorithms have been proposed. Among all the implemented algorithms, we achieved the best result of almost 90%. Compared to other algorithms that have been implemented for the same purpose and have been used and tested on the same database, this can be considered a satisfactory and reliable result. In conclusion, the main objective of this work was to provide an efficient, secure, and reliable solution in the field of biometric fingerprint spoof detection.