With the broad application of iris recognition systems, iris images acquired under cross-sensor and cross-spectrum conditions are increasingly common. Differences in the information stored in these images place high requirements for accuracy and robustness on cross-domain iris recognition algorithms, and current algorithms have not performed well in cross-domain iris recognition. Hence, we propose a dense squeeze and excitation network (DenseSENet) to extract common features in cross-domain iris images and use real-valued features for cross-domain matching. DenseSENet is a deep convolutional neural network based on an attention mechanism. It uses the batch normalization-rectified linear unit-Conv1  ×  1 structure to increase the nonlinearity of the network and repeatedly uses features through dense connections to effectively prevent overfitting. The proposed dense squeeze and excitation structure extracts more accurate and stable features by learning the relations between channels and assigning corresponding attention values. The structure of global average pooling-fully connected-softmax is utilized to obtain compact and normalized real-valued feature templates to effectively reduce the size of the iris template. The proposed cross-domain iris recognition framework based on DenseSENet is end-to-end, which transforms the cross-domain matching problem to a many-to-one classification problem. Experiments are conducted on publicly available cross-sensor and cross-spectral iris databases. The results show that the proposed real-valued feature matching based on DenseSENet has better matching accuracy and robustness than the state-of-art cross-domain matching algorithm on two publicly available cross-sensor and cross-spectral iris databases.