Thermal Face Recognition Under Temporal Variation Conditions

In this paper, we analyze the problems produced by temporal variations of infrared face images when used in face recognition systems. The temporal variations present in thermal face images are mainly due to different environmental conditions, physiological changes of the subjects, and differences of the infrared detectors' responsivity at the time of the capture, which affect the performance of infrared face recognition systems. To perform this paper, we created two thermal face databases that include capture sessions with real and variable conditions. We also propose two criteria to quantify the temporal variations between data sets. The thermal face recognition systems have been developed using the following five methods: local binary pattern (LBP), Weber linear descriptor (WLD), Gabor jet descriptors, scale invariant feature transform, and speeded up robust features. The results indicate that the local matching-based methods (WLD and LBP) are mostly immune to temporal variations, which is noticeable when the face images have been acquired with a time lapse, while the rest of the methods are clearly affected and are not suitable for practical infrared face recognition.