Thermal Infrared Face Recognition Based on the Modified Blood Perfusion Model and Improved Weber Local Descriptor

In order to extract the robust thermal infrared facial features, a novel method based on the modified blood perfusion model and the improved Weber local descriptor is proposed. Weber local descriptor (WLD) is able to extract a wealth of local texture information, which computes not only the differences between the center pixel and its neighbors but also the gradient orientation information describing the direction of edges in the local area, so it is suitable for texture-based thermal infrared face recognition. In order to make full use of local authentication information, an improved Weber local descriptor is proposed to extract the local features from the blood perfusion image. For improved Weber local descriptor, the Isotropic Sobel operator instead of the traditional method is used to compute the orientation and build more stable feature histograms. Experimental results show that the proposed method could achieve better recognition performance compared to the traditional methods.