The recognition of biometrics is an attractive research field in computer science and technology. As a soft biometric, the iris has the advantages of uniqueness, stability and anti-counterfeiting. Recognizing the gender of a person from the iris image is used in identity verification and security. Monitoring and other fields have broad application prospects. Aiming at the shortcomings of traditional machine learning and shallow neural networks in gender classification of iris image and the advantages of convolutional neural networks in image feature extraction, a residual network (ResNet)-based gender classification of iris image model is proposed, which uses ResNet combined with transfer learning is used for pre-training on ImageNet image dataset. The model is used to train an end-to-end iris image gender classifier on the dataset, the accuracy rate reaches 94.6%. Comparing the trained model with other related models on the same dataset, the results show that the test accuracy and recognition efficiency of this model are better than other models.