Face recognition has made significant advances with the development of deep learning and has begun to be deployed in some unrestricted scenarios. Many smartphones, for example, have infrared sensors that allow them to capture clear images even in low-light conditions. Face authentication under complex environmental conditions can thus be accomplished by matching NIR-VIS face images across modalities. However, existing NIR-VIS datasets lack enough variation in face attributes and are insufficient for real-world scenarios. To address the aforementioned issues, we first propose a 300-person NIR-VIS cross-modality face dataset with a variety of attributes. Based on modal information removal, we proposed a NIR-VIS cross-modal face recognition model. We can effectively extract modal information by constraining the similarity distribution of modalities and then using the orthogonal loss to remove modal information from identity features. The method achieves excellent results on our dataset and CASIA NIR-VIS 2.0 dataset.