Near-infrared (NIR) face recognition (FR) has demonstrated robustness against changes in ambient illumination, which makes it suitable for surveillance even under weak illumination conditions. However, the existing database for NIR FR only contains frontal face images, and the impact of pose variation on the robustness of NIR FR remains unascertained. We developed an NIR face database with 57 pose variations in a dark environment, which can be used in pose-invariant FR research. Convolutional neural networks (CNNs) were designed and tested in comparison to the traditional method in the database. The experimental results showed that a difference of even 10 deg between the gallery and testing sets can dramatically reduce the recognition performance. Additionally, an average accuracy of 90.58% was obtained for pose-invariant recognition by employing more pose variations in the gallery set using the CNN-based method.