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.

中文翻译：

---

SWU-NIRPV：近红外姿态变化人脸数据库和姿态不变人脸识别

近红外（NIR）人脸识别（FR）表现出了抵抗环境光照变化的鲁棒性，使其即使在弱光照条件下也适用于监视。但是，现有的NIR FR数据库仅包含正面图像，并且姿势变化对NIR FR鲁棒性的影响仍然不确定。我们开发了一个NIR人脸数据库，该数据库在黑暗环境中具有57个姿势变化，可用于姿势不变FR研究中。与数据库中的传统方法相比，设计和测试了卷积神经网络（CNN）。实验结果表明，图库和测试集之间即使相差10度也会大大降低识别性能。此外，平均准确度为90。