A palmprint is a small part of the palm flat that contains additional characteristics that can be used in authentication systems. It also has the property of permanence, which indicates that it will not alter through time. However, extracting the deepest and useful features from palmprint is a critical point. Most of the recently developed methods use principal lines, wrinkles, and creases, which is not enough to distinguish two people due to closeness. Recently, deep learning methods have been considered as an important key point for these kinds of tasks in order to extract deep features like texture features. We present a deep convolutional neural networks (CNN) that is specifically designed to suit palmprint images in order to achieve secure authentication processes. The COEP palmprint database was used in the experiments, and the accuracy measure as well as the F1-score were used in the evaluation process. The proposed model had a high level of accuracy, with a score of 97.55 percent. Palmprint authentication is performed efficiently using the described method.