<![CDATA[Fingerprint recognition is one of the technological developments that is feasible since childhood. Along with the increasing number of toddlers, an introduction system is needed that is able to uniquely identify toddlers with the biometric patterns they have. Toddler fingerprint patterns have a low contrast between ridges and valleys and have a size (distance between ridges) that is smaller than adult fingerprints making it difficult to design accurate algorithms that are able to extract important features and match them in a strong way. In this study, the process begins with pre-processing and then uses the Zone Based Linear Pattern method to extract features on toddler fingerprints and the Extreme Learning Machine (ELM) classification method to recognize the identity of the fingerprint owner. The test results using a combined binary pattern for the Zone Based Linear Binary Pattern extraction method, the gaussian filtering, opening and adaptive thresholding technique for pre-processing images with dimensions of 200x200 in the image, the z-score method for normalizing data and the number of hidden neurons by 50 with binary sigmoid activation function for ELM classification produces the best accuracy of 72.33%. Based on these results it can be concluded that the Zone Based Linear Binary Pattern and Extreme Learning Machine methods can be used to recognize toddler fingerprints.]]>
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