Study of Local Matching-Based Facial Recognition Methods Using Thermal Infrared Imagery

This paper shows a comparative study among different local matching-based methods for thermal infrared face recognition. The principal assumption of this work is that the thermal face corresponds to the diffuse energy emission captured by an infrared camera, where the thermal signature is unique for each subject and it can be addressed as a texture descriptor with thermal images. Local matching-based methods find inter-class differences that improve the face recognition rate in thermal spectrum. Specifically, this work considers four methods: Local Binary Pattern (LBP), Local Derivative Pattern (LDP), Weber Linear Descriptor (WLD) and Histograms of Oriented Gradients Descriptors (HOG). The methods are evaluated and compared using the UCHThermalFace database, that considers real-world conditions and unconstrained environments, such as indoor and outdoor setups, natural variations in illumination, facial expression, pose, accessories, occlusions, and background. Results indicate that HOG variants followed by LBP method achieved the best recognition rates for face recognition systems.