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Facial recognition via thermal imaging

Thermal to visible process captures image features in the dark and reliably compares thermal features to visible light features enabling wider use of image databases

Single subject as viewed in visible (a) and thermal (b-e) spectra. (Top) The Stokes images, 𝑆0S0, 𝑆1S1, 𝑆2S2, are shown in (b), (c), and (d), respectively. The degree of linear polarization image is shown in (e). (Middle) Images after difference of Gaussian filtering, where high/low spatial frequencies have been attenuated. (Bottom) Histogram of oriented gradients feature representations, where grayscale intensity represents magnitude of local edge direction.

Automatic facial recognition has a wide range of applications in the commercial, military, and government sectors, spanning from tagging people in social networking websites to surveillance for homeland security. To date, face recognition research has predominantly focused on the visible spectrum, addressing challenges such as illumination variations, pose, and image resolution. However, for surveillance during nighttime the lack of illumination prevents cameras operating in the visible-light spectrum from being used discreetly and effectively.

Thermal imaging measures radiation in the mid-wave infrared (MWIR) and long-wave infrared (LWIR) spectra, which is naturally emitted by living tissue, and therefore is a highly practical imaging modality for nighttime operation. However, as most databases and watch lists only contain facial imagery in the visible spectrum, it is difficult to match an unknown thermal image to a set of known visible images. This is referred to as cross-modal face recognition.

The Army’s solution to the above problem is a cross-modal face matching system using polarimetric thermal image data. Polarimetric imaging in the thermal spectrum is sensitive to changes in surface texture and geometry. The polarization-state of radiation emission provides geometric and texture information about the surface of the imaged face. For cross-modal recognition, the combination of polarimetric face features with conventional thermal face features provides a stronger correlation with the visible light feature representation and leads to better matching results than conventional thermal imaging alone.

The novel method comprises the input of many polarimetric images of a face acquired by a thermal imaging camera, extraction of features from each of the images to generate feature vectors for each of the images, creating a composition of the feature vectors for each of the images together to form one composite image, and cross-matching the composite with other feature vectors in order to determine a match.

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