In order to address the issues of incomplete collection, vulnerability to attacks, and limitations in specific recognition scenarios in single modal biometric information, a multi-level fusion recognition model for faces and iris was proposed, a multi-modal biometric recognition system was designed and implemented to integrate the proposed model in a modular manner. The lightweight convolutional neural networks was used as feature extractors, intra class correlations between different modal features was utilized on the feature level, normalizing and concatenating the features of different modalities; the minimum strategy was used to fuse left and right iris scores on the score layer, the average strategy was used to fuse iris scores and face scores. Homologous multi-modal datasets was extracted from the CASIA-IrisV4-Distance dataset for experiment verification, feature layer fusion algorithm and score layer fusion algorithm both achieves an accuracy of 99.8%. It is observed in the experiment that this system has robustness and generalization.