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21 May 2012 Thermal to visible face recognition
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In low light conditions, visible light face identification is infeasible due to the lack of illumination. For nighttime surveillance, thermal imaging is commonly used because of the intrinsic emissivity of thermal radiation from the human body. However, matching thermal images of faces acquired at nighttime to the predominantly visible light face imagery in existing government databases and watch lists is a challenging task. The difficulty arises from the significant difference between the face's thermal signature and its visible signature (i.e. the modality gap). To match the thermal face to the visible face acquired by the two different modalities, we applied face recognition algorithms that reduce the modality gap in each step of face identification, from low-level analysis to machine learning techniques. Specifically, partial least squares-discriminant analysis (PLS-DA) based approaches were used to correlate the thermal face signatures to the visible face signatures, yielding a thermal-to-visible face identification rate of 49.9%. While this work makes progress for thermal-to-visible face recognition, more efforts need to be devoted to solving this difficult task. Successful development of a thermal-to-visible face recognition system would significantly enhance the Nation's nighttime surveillance capabilities.
© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jonghyun Choi, Shuowen Hu, S. Susan Young, and Larry S. Davis "Thermal to visible face recognition", Proc. SPIE 8371, Sensing Technologies for Global Health, Military Medicine, Disaster Response, and Environmental Monitoring II; and Biometric Technology for Human Identification IX, 83711L (21 May 2012);

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