2005 · 171 citations · 9 references
ComputingVision ScienceComputer VisionMachine VisionThermal AnalysisFace PerceptionFace RecognitionImage ClassificationThermal SensorsFace VerificationIllumination EffectsIllumination InvarianceInfrared Image SensorsReflectivityThermal Imaging For IdentificationThermal Infrared ImagingThermal Sensing
Accurate face recognition under variable illumination is challenging because conventional cameras capture reflected light that mixes skin reflectivity with external lighting, whereas thermal emission from skin provides an intrinsic measurement independent of illumination. The study investigates whether long‑wave infrared (LWIR) images are invariant to illumination by comparing the performance of two established face‑recognition algorithms on LWIR versus visible imagery. To enable this comparison, the authors devised rigorous data‑collection protocols that formalize face‑recognition experiments for computer vision in the thermal infrared.
A key problem for face recognition has been accurate identification under variable illumination conditions. Conventional video cameras sense reflected light so that image grayvalues are a product of both intrinsic skin reflectivity and external incident illumination, thus obfuscating the intrinsic reflectivity of skin. Thermal emission from skin, on the other hand, is an intrinsic measurement that can be isolated from external illumination. We examine the invariance of Long-Wave InfraRed (LWIR) imagery with respect to different illumination conditions from the viewpoint of performance comparisons of two well-known face recognition algorithms applied to LWIR and visible imagery. We develop rigourous data collection protocols that formalize face recognition analysis for computer vision in the thermal IR.
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Matthew Turk, Alex Pentland · Journal of Cognitive Neuroscience · 1991 · 13.8K citations · Full text
Comparison of visible and infra-red imagery for face recognition
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Color Night Vision: Opponent Processing in the Fusion of Visible and IR Imagery
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