Face identification systems that operate on long wave infrared (LWIR) images are able to overcome some of the limitations of approaches based on visible images, such as dealing effectively with illumination variations. Nonetheless, distortions of perceptual image quality can impair the performance of thermal face recognition systems. Although the interaction between perceptual image quality and tasks such as face detection has been studied on visual images, the development of similar models has not been applied to the LWIR-based face recognition problem. Here, we analyze the impact of four common infrared image distortions (gaussian noise, blur, non-uniformity, and JPEG compression) on two thermal face recognition systems. We propose an LWIR image face recognition framework, based on thermal signature templates, and enhanced by natural scene statistics image quality descriptors, which achieves system robustness against image quality distortions. Furthermore, we develop a novel infrared face recognition system that is based on the complex wavelet structural similarity (CW-SSIM) index, which exhibits resistance to image distortions, within a relatively simple implementation. Our results validate the applicability of image quality assessment models to biometric tasks on LWIR images. To facilitate our study, we created two new LWIR facial image databases, with different poses, expressions and illumination conditions.

在长波红外（LWIR）图像上运行的面部识别系统能够克服基于可见图像的方法的某些局限性，例如有效地处理照明变化。然而，感知图像质量的失真会损害热面部识别系统的性能。尽管已经在视觉图像上研究了感知图像质量与诸如面部检测之类的任务之间的交互作用，但相似模型的开发尚未应用于基于LWIR的面部识别问题。在这里，我们分析了四个常见的红外图像失真（高斯噪声，模糊，不均匀性和JPEG压缩）对两个热面部识别系统的影响。我们提出了一个基于热特征模板的LWIR图像人脸识别框架，并通过自然场景统计图像质量描述符进行了增强，从而实现了针对图像质量失真的系统鲁棒性。此外，我们开发了一种基于复杂小波结构相似性（CW-SSIM）指数的新型红外人脸识别系统，该系统在相对简单的实现中即可抵抗图像失真。我们的结果验证了图像质量评估模型对LWIR图像上的生物统计任务的适用性。为了促进我们的研究，我们创建了两个新的LWIR面部图像数据库，它们具有不同的姿势，表情和照明条件。在相对简单的实现方式中，它表现出对图像失真的抵抗力。我们的结果验证了图像质量评估模型对LWIR图像上的生物统计任务的适用性。为了促进我们的研究，我们创建了两个新的LWIR面部图像数据库，它们具有不同的姿势，表情和照明条件。在相对简单的实现方式中，它表现出对图像失真的抵抗力。我们的结果验证了图像质量评估模型对LWIR图像上的生物统计任务的适用性。为了促进我们的研究，我们创建了两个新的LWIR面部图像数据库，它们具有不同的姿势，表情和照明条件。