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Face Recognition in Hyperspectral Images
December 2003 (vol. 25 no. 12)
pp. 1552-1560
ASCII Text
x 
 
Zhihong Pan, Glenn Healey, Manish Prasad, Bruce Tromberg, "Face Recognition in Hyperspectral Images," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 12, pp. 1552-1560, December, 2003.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2003.1251148,
author = {Zhihong Pan and Glenn Healey and Manish Prasad and Bruce Tromberg},
title = {Face Recognition in Hyperspectral Images},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {25},
number = {12},
issn = {0162-8828},
year = {2003},
pages = {1552-1560},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2003.1251148},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - Face Recognition in Hyperspectral Images
IS - 12
SN - 0162-8828
SP1552
EP1560
EPD - 1552-1560
A1 - Zhihong Pan,
A1 - Glenn Healey,
A1 - Manish Prasad,
A1 - Bruce Tromberg,
PY - 2003
KW - Face recognition
KW - hyperspectral.
VL - 25
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 

Abstract—Hyperspectral cameras provide useful discriminants for human face recognition that cannot be obtained by other imaging methods. We examine the utility of using near-infrared hyperspectral images for the recognition of faces over a database of 200 subjects. The hyperspectral images were collected using a CCD camera equipped with a liquid crystal tunable filter to provide 31 bands over the near-infrared (0.7\mum-1.0\mum). Spectral measurements over the near-infrared allow the sensing of subsurface tissue structure which is significantly different from person to person, but relatively stable over time. The local spectral properties of human tissue are nearly invariant to face orientation and expression which allows hyperspectral discriminants to be used for recognition over a large range of poses and expressions. We describe a face recognition algorithm that exploits spectral measurements for multiple facial tissue types. We demonstrate experimentally that this algorithm can be used to recognize faces over time in the presence of changes in facial pose and expression.

[1] G. Healey and D. Slater, Models and Methods for Automated Material Identification in Hyperspectral Imagery Acquired under Unknown Illumination and Atmospheric Conditions IEEE Trans. Geoscience and Remote Sensing, vol. 37, no. 6, pp. 2706-2717, Nov. 1999.[2] G. Healey, Lessons Learned: Technology Transfer from Terrestrial Spectroscopy to Biomedicine Spectral Imaging: Instrumentation, Applications, and Analysis (SPIE), vol. 3920, Jan. 2000.[3] K. Etemad and R. Chellappa, Discriminant Analysis for Recognition of Human Face Images J. Optical Soc. Am. A, vol. 14, pp. 1724-1733, 1997.[4] B. Moghaddam and A. Pentland, “Probabilistic Visual Learning for Object Representation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 696-710, July 1997.[5] L. Wiskott, J.M. Fellous, N. Kruger, and C. von der Malsburg, Face Recognition by Elastic Bunch Graph Matching IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 775-779, July 1997.[6] J. Wilder, Face Recognition Using Transform Coding of Grayscale Projection and the Neural Tree Network Artifical Neural Networks with Applications in Speech and Vision, R. Mammone, ed. Chapman Hall, pp. 520-536, 1994.[7] D.L. Swets and J. Weng, Using Discriminant Eigenfeatures for Image Retrieval IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 16, no. 8, pp. 831-836, Aug. 1996.[8] P. Phillips, H. Wechsler, J. Huang, and P. Rauss, The FERET Database and Evaluation Procedure for Face Recognition Algorithms Image and Vision Computing, vol. 16, no. 5, pp. 295-306, 1998.[9] P.J. Phillips, H. Moon, and S.A. Rozvi, The FERET Evaluation Methodolody for Face Recognition Algorithms IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 10, pp. 1090-1104, Oct. 2000.[10] R. Gross, J. Shi, and J. Cohn, Quo Vadis Face Recognition? Technical Report CMU-RI-TR-01-17, Robotics Inst., Carnegie-Mellon Univ., June 2001.[11] R. Gross, I. Matthews, and S. Baker, Appearance-Based Face Recognition and Light-Fields Technical Report CMU-RI-TR-02-20, Robotics Inst., Carnegie-Mellon Univ., Aug. 2002.[12] A. Martínez, Recognizing Imprecisely Localized, Partially Occluded and Expression Variant Faces from a Single Sample per Class IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 6, pp. 748-763, 2002.[13] J. Wilder, P. Phillips, C. Jiang, and S. Wiener, Comparison of Visible and Infra-Red Imagery for Face Recognition Proc. IEEE Int'l Conf. Automatic Face and Gesture Recognition, pp. 182-187, 1996.[14] D. Socolinsky, L. Wolff, J. Neuheisel, and C. Eveland, Illumination Invariant Face Recogntion Using Thermal Infrared Imagery Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 527-534, 2001.[15] D. Socolinsky and A. Selinger, A Comparative Analysis of Face Recognition Performance with Visible and Thermal Infrared Imagery Proc. Int'l Conf. Pattern Recognition, 2002.[16] V. Blanz, S. Romdhani, and T. Vetter, Face Identification across Different Poses and Illuminations with a 3D Morphable Model Proc. IEEE Int'l Conf. Automatic Face and Gesture Recognition, pp. 202-207, 2002.[17] V. Tuchin, Tissue Optics: Light Scattering Methods and Instruments for Medical Diagnosis. Bellingham, Wash.: SPIE Press, 2000.[18] R. Anderson and J. Parrish, The Optics of Human Skin J. Investigative Dermatology, vol. 77, no. 1, pp. 13-19, 1981.[19] M. van Gemert, S. Jacques, H. Sternborg, and W. Star, Skin Optics IEEE Trans. Biomedical Eng., vol. 36, no. 12, pp. 1146-1154, Dec. 1989[20] E. Edwards and S. Duntley, The Pigments and Color of Living Human Skin Am. J. Anatomy, vol. 65, pp. 1-33, 1939.[21] N. Tsumura, H. Haneishi, and Y. Miyake, Independent-Component Analysis of Skin Color Image J. Optical Soc. Am. A, vol. 16, no. 9, pp. 2169-2176, 1999.[22] E. Angelopoulou, R. Molana, and K. Danilidis, Multispectral Skin Color Modelling Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 635-642, 2001.[23] M. Störring, H. Andersen, and E. Granum, Skin Colour Detection under Changing Lighting Conditions Proc. Seventh Symp. Intelligent Robotics Systems, July 1999.[24] P. Debevec et al., "Acquiring the Reflectance Field of a Human Face," Computer Graphics (Proc. Siggraph 2000), ACM Press, New York, 2000, pp. 145-156.[25] M. Hiraoka, M. Firbank, M. Essenpreis, M. Cope, S. Arridge, P. van der Zee, and D. Delpy, A Monte Carlo Investigation of Optical Pathlength in Inhomogeneous Tissue and Its Application to Near-Infrared Spectroscopy Physics in Medicine and Biology, vol. 38, pp. 1859-1876, 1993.[26] P. Bargo, T. Goodell, R. Sleven, G. Kovall, G. Blair, and S. Jacques, Optical Measurements for Quality Control During Photodynamic Therapy Plenary Talk Int'l Photodynamic Assoc. Meeting, June 2001.[27] R. Siegel and J. Howell, Thermal Radiation Heat Transfer. McGraw-Hill, 1981.[28] N. Gat, Imaging Spectroscopy Using Tunable Filters: A Review Proc. SPIE Conf. Algorithms for Multispectral and Hyperspectral Imagery VI, Apr. 2000.[29] R. Duda, P. Hart, and D. Stork, Pattern Classification. New York: John Wiley&Sons, 2001.

Index Terms:
Face recognition, hyperspectral.
Citation:
Zhihong Pan, Glenn Healey, Manish Prasad, Bruce Tromberg, "Face Recognition in Hyperspectral Images," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 12, pp. 1552-1560, Dec. 2003, doi:10.1109/TPAMI.2003.1251148
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