We examine the performance of illumination-invariant face recognition in outdoor hyperspectral images using a database of 200 subjects. The hyperspectral camera acquires 31 bands over the 700-1000nm spectral range. Faces are represented by local spectral information for several tissue types. Illumination variation is modeled by low-dimensional spectral radiance subspaces. Invariant subspace projection over multiple tissue types is used for recognition. The experiments consider various face orientations and expressions. The analysis includes experiments for images synthesized using face reflectance images of 200 subjects and a database of over 7,000 outdoor illumination spectra. We also consider experiments that use a set of face images that were acquired under outdoor illumination conditions.
Hyperspectral sensors provide useful discriminants for human face recognition that cannot be obtained by other imaging methods. Near-infrared spectral measurements allow the sensing of subsurface tissue structure which is significantly different from person to person but relatively stable over time. The spectral properties of human tissue are also nearly invariant to changes in face orientation which bring significant degradation to most other face recognition algorithms. We examine the utility of using near-infrared hyperspectral images for the recognition of human subjects over a database of 200 subjects. The face recognition algorithm exploits spectral measurements for individual facial tissue types and combinations of facial tissue types. We demonstrate experimentally that hyperspectral imaging promises to support face recognition independent of facial expression and orientation.
We examine the performance of illumination-invariant face recognition in hyperspectral images on a database of 200 subjects. The images are acquired over the near-infrared spectral range of 0.7-1.0 microns. Each subject is imaged over a range of facial orientations and expressions. Faces are represented by local spectral information for several tissue types. Illumination variation is modeled by low-dimensional linear subspaces of reflected radiance spectra. One hundred outdoor illumination spectra measured at Boulder, Colorado are used to synthesize the radiance spectra for the face tissue types. Weighted invariant subspace projection over multiple tissue types is used for recognition. Illumination-invariant face recognition is tested for various face rotations as well as different facial expressions.
We examine the utility of using near-infrared hyperspectral images for the recognition of human subjects over a database of 137 subjects. Hyperspectral images were collected using a CCD camera equipped with a liquid crystal tunable filter and calibrated to spectral reflectance. The face recognition algorithm exploits spectral measurements for individual facial tissue types and combinations of facial tissue types. We demonstrate experimentally that hyperspectral images provide the opportunity to recognize faces independent of facial expression and face orientation.
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