Matrix computations (3rd ed.)
Flexible images: matching and recognition using learned deformations
Computer Vision and Image Understanding - Special issue on physics-based modeling and reasoning in computer vision
What Is the Set of Images of an Object Under All Possible Illumination Conditions?
International Journal of Computer Vision
The Quotient Image: Class-Based Re-Rendering and Recognition with Varying Illuminations
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Lambertian Reflectance and Linear Subspaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Temporal Image Sequence
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal Linear Representations of Images for Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Face Recognition via Wavelets and Mathematical Morphology
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
Multiresolution face recognition
Image and Vision Computing
A multiresolution manifold distance for invariant image similarity
IEEE Transactions on Multimedia
Advances in matrix manifolds for computer vision
Image and Vision Computing
Low-resolution face recognition: a review
The Visual Computer: International Journal of Computer Graphics
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Recent work has established that digital images of a human face, collected under various illumination conditions, contain discriminatory information that can be used in classification. In this paper we demonstrate that sufficient discriminatory information persists at ultralow resolution to enable a computer to recognize specific human faces in settings beyond human capabilities. For instance, we utilized the Haar wavelet to modify a collection of images to emulate pictures from a 25- pixel camera. From these modified images, a low-resolution illumination space was constructed for each individual in the CMU-PIE database. Each illumination space was then interpreted as a point on a Grassmann manifold. Classification that exploited the geometry on this manifold yielded error-free classification rates for this data set. This suggests the general utility of a low-resolution illumination camera for set-based image recognition problems.