Partial fingerprint recognition has become crucial to identifying a user's authenticity in mobile device transactions. As a result, developments are increasing for more effective and accurate identification and authentication of a user using a scanner that captures a small fingerprint image. However, there is a reduction in the number of features from a full fingerprint to a partial fingerprint image during partial to partial fingerprint matching. Therefore, we propose a method combining deep learning and feature descriptors for partial fingerprint recognition. The matching score is obtained by the weighted combination of the scores from deep learning and feature descriptors. Experiments have been carried out with data variations such as the image size, epoch numbers and dataset types. The proposed method of combining deep learning and feature descriptors in the matching score evaluation process has obtained good results for the FVC2002 DB1, DB2 and DB3 datasets.