This research study proposes a unique deep learning classifier using palm hand’s principle lines extraction approach for the palmprint recognition system. A Deep Convolution Multifractal Analysis Model for Palmprint Recognition (DCMAPR) is proposed to reveal the novelty. In pre-processing, the principle line feature is extracted using morphological operations and edge detection algorithm. For the feature extraction technique, multifractal analysis is used. To perform the techniques, Box-counting and Gliding-Box algorithms are performed for multifractal analysis. To more accurately authenticate the real person of the captured palmprint, classify this feature vector using Convolution Neural Network (CNN) classifier technique. The multi-spectral 2D-PROI image database used in this study came from POLYU, the Hong Kong Polytechnic University in Hong Kong. The proposed scheme has undergone scrutiny and evaluation using numerous criteria, and it has been determined to have 99.25% authentication accuracy.