为了解决单模态生物特征信息采集不全、易被攻击以及特定识别场景下受限等问题，构建了一个针对人脸和虹膜的多层次融合识别模型，设计并实现多模态生物特征识别系统将所提模型以模块的方式进行集成。所提模型使用轻量级卷积神经网络作为特征提取器，在特征层利用不同模态特征之间的类内相关性，对不同模态的特征归一化后串联；在分数层使用最小值策略融合左右虹膜得分，使用平均值策略融合虹膜得分和人脸得分。从CASIA-IrisV4-Distance数据集中提取同源多模态数据集进行实验验证，特征层融合算法和分数层融合算法准确率均达到99.8%。实验表明，该系统具有鲁棒性和泛化性。

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.