Abstract
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 ultra-low 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.
This study was partially supported by the National Science Foundation under award DMS-0434351 and the DOD-USAF-Office of Scientific Research under contract FA9550-04-1-0094. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation or the DOD-USAF-Office of Scientific Research.
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Chang, JM., Kirby, M., Kley, H., Peterson, C., Draper, B., Beveridge, J.R. (2007). Recognition of Digital Images of the Human Face at Ultra Low Resolution Via Illumination Spaces. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds) Computer Vision – ACCV 2007. ACCV 2007. Lecture Notes in Computer Science, vol 4844. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76390-1_72
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DOI: https://doi.org/10.1007/978-3-540-76390-1_72
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