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- to 1000-nm spectral range. Faces are represented by local spectral information for several tissue types. Illumination variation is modeled by low-dimensional spectral radiance subspaces. Weighted 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 from indoor face reflectance images of 200 subjects, using a database of more than 7,000 outdoor illumination spectra. We also examine a set of images of 10 subjects of the 200 that were acquired under outdoor conditions using a calibrated hyperspectral camera.
Spectral reflectance properties of local facial regions have been shown to be useful discriminants for face recognition. To evaluate the performance of spectral signature methods versus purely spatial methods, face recognition tests are conducted using the eigenface method for single-band images extracted from the hyperspectral images. This is the first such comparison based on the same dataset. Selected sets of bands as well as PCA transformed bands are also used for face recognition evaluation with individual band processed separately. A new spectral eigenface method which preserves both spatial and spectral features is proposed. All algorithms based on spectral and/or spatial features are evaluated under the same framework and are compared in terms of accuracy and computational efficiency.
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.
KEYWORDS: 3D modeling, Hyperspectral imaging, Sensors, Detection and tracking algorithms, RGB color model, 3D image processing, Atmospheric modeling, Object recognition, Data modeling, Feature extraction
We present models and algorithms for recognizing 3D objects in airborne 0.4-2.5 micron hyperspectral images acquired under unknown conditions. Objects of interest exhibit complex geometries with surfaces of different materials. The DIRSIG image generation software is used to build spatial/spectral surfaces of different materials. The DIRSIG image generation software is used to build spatial/spectral subspace models for the objects that capture a range of atmospheric and illumination conditions and viewing geometries. Since we consider scales for which multiple materials will mix in a pixel, the object subspace models also account for spectral mixing. An important aspect of the work is the use of methods for partitioning object subspaces to optimize performance. The new algorithms have been evaluated using hyperspectral data that has been synthesized for a range of conditions.
Hyperspectral sensors provide useful discriminant for human face identification that cannot be obtained by other sensing modalities. The spectral properties of human tissue vary significantly from person to person. While the visible spectral characteristics of a person's skin may change over time, near-infrared spectral measurements allow the sensing of subsurface tissue change over time, near-infrared spectral measurements allow the sensing of subsurface tissue structure that is difficult for a subject to modify. The high spectral dimensionality of hyper-spectral imagery provides the opportunity to recognize subpixel features which enables reliable identification at large distances. We propose methods for the identification of humans using properties of individual tissue types as well as combinations of tissue types. Intrinsic models for facial tissue types for a person can be constructed form a single hyperspectral image. These models can be used to generate spectral subspaces that model the set of spectra for a face over a range of facial orientations, environmental conditions, and spectral mixtures.
KEYWORDS: Scanners, Printing, RGB color model, Nonimpact printing, Inkjet technology, Color difference, Color reproduction, 3D scanning, Photography, Laser scanners
Due to the increasing popularity and afford ability of color imaging devices, color characterization for these devices becomes an important subject. In other words, a set of color profile(s) needs to be generated for each device to transform the device dependent color space to a device independent one. This paper will concentrate on color characterization of scanners.
We analyze a set of 7,258 0.4-2.2 micron ground spectral irradiance functions measured on different days over a wide range of conditions. We show that a low-dimensional linear model can be used to capture the variability in these measurements. Using this linear model, we compare the data with a previous empirical study. We also examine the agreement of the data with spectra generated by MODTRAN 4.0. Using a database of 224 materials, we consider the implications of the observed spectral variability for hyperspectral material discrimination using subspace projection techniques.
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