Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  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), ber 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 follod by LBP method achieved the best recognition rates for face recognition systems.
['Gabriel Hermosilla', 'Gonzalo Farias', 'Cesar Martin', 'Francisco Gallardo']
I am writing a research paper on a idea Study of Local Matching-Based Facial Recognition Methods Using Thermal Infrared Imagery
[{"from": "human", "value": "I am writing a research paper on a idea Study of Local Matching-Based Facial Recognition Methods Using Thermal Infrared Imagery"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  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), ber 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 follod by LBP method achieved the best recognition rates for face recognition systems."}]