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Biometrics We introduce wavelet packet correlation filter classifiers.
Correlation filters are traditionally designed in the image domain by
minimization of some criterion function of the image training set. Instead,
we perform classification in wavelet spaces that have training set
representations that provide better solutions to the optimization problem in
the filter design. We propose a pruning algorithm to find these wavelet
spaces by using a correlation energy cost function, and we describe a match
score fusion algorithm for applying the filters trained across the packet
tree. The proposed classification algorithm is suitable for any object
recognition task. We present results by implementing a biometric recognition
system that uses the NIST 24 fingerprint database, and show that applying
correlation filters in the wavelet domain results in considerable improvement
of the standard correlation filter algorithm. |
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Overview Sponsors Some of this material is
based upon work supported by the PA State Tobacco Settlement, Kamlet-Smith Bioinformatics Grant. Any opinions, findings, and
conclusions or recommendations expressed in this material are those of the
author(s) and do not necessarily reflect the views of the sponsor(s). Collaborators Pablo Hennings Yeomans, Jason
Thornton, Vijayakumar Bhagavatula Research
Corner Wavelet packet correlation methods in biometrics Teaching
Corner Links |
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Adaptive Multiresolution Methods for Biometrics We introduce wavelet packet
correlation filter classifiers. Correlation filters are traditionally
designed in the image domain by minimization of some criterion function of
the image training set. Instead, we perform classification in wavelet spaces
that have training set representations that provide better solutions to the
optimization problem in the filter design. We propose a pruning algorithm to
find these wavelet spaces by using a correlation energy cost function, and we
describe a match score fusion algorithm for applying the filters trained
across the packet tree. The proposed classification algorithm is suitable for
any object recognition task. We present results by implementing a biometric
recognition system that uses the NIST 24 fingerprint database, and show that
applying correlation filters in the wavelet domain results in considerable
improvement of the standard correlation filter algorithm. P.
Hennings Yeomans, J. Thornton, J. Kovačević and B.V.K.V. Kumar, "Wavelet packet correlation methods in
biometrics'', Applied Optics, special
issue on Biometric Recognition Systems, vol. 44, no. 5, February 2005., pp.
637-646. J.T.
Thornton, P. Hennings Yeomans, J. Kovačević and B.V.K.V. Kumar, ''Wavelet
packet correlation methods in biometrics'',
Proc. IEEE Int. Conf. Acoust., Speech, and Signal Proc., Philadelphia, PA,
March 2005., pp. II:81-84. An Adaptive Multiresolution Approach to
Fingerprint Recognition We propose an adaptive
multiresolution (MR) approach to the classification of fingerprint images.
The system adds MR decomposition in front of a generic classifier consisting
of feature computation and classification in each MR subspace, yielding local
decisions, which are then combined into a global decision using a weighting
algorithm. In our previous work on classification of protein subcellular
location images, we showed that the space-frequency localized information in
the MR subspaces adds significantly to the discriminative power of the
system. Here, we go one step farther; We develop a new weighting method which
allows for the discriminative power of each subband to be expressed and
examined within each class. This, in turn, allows us to evaluate the
importance of the information contained within a specific subband. Moreover,
we develop a pruning procedure to eliminate the subbands
that do not contain useful information.
This leads to potential identification of the appropriate MR
decomposition both on a per class basis and for a given dataset. With this new approach, we make the
system adaptive, flexible as well as more accurate and efficient. A. Chebira, L. P. Coelho, A. Sandryhalia, S. Lin, G. W. Jenkinson, J. MacSleyne, C. Hoffman, P. Cuadra, C. Jackson, M. Püschel and J. Kovačević, "An Adaptive Multiresolution Approach to Fingerprint Recognition", Proc. IEEE Conf. on Image Proc., San Antonio, TX, Sep. 2007. To appear. |
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A. Chebira, L. P. Coelho, A. Sandryhalia, S. Lin, G. W. Jenkinson, J. MacSleyne, C. Hoffman, P. Cuadra, C. Jackson, M. Püschel and J. Kovačević, "An Adaptive Multiresolution Approach to Fingerprint Recognition", Proc. IEEE Conf. on Image Proc., San Antonio, TX, Sep. 2007. To appear. P.
Hennings Yeomans, J. Thornton, J. Kovačević and B.V.K.V. Kumar, "Wavelet packet correlation methods in
biometrics'', Applied Optics, special issue
on Biometric Recognition Systems, vol. 44, no. 5, February 2005., pp.
637-646. J.T.
Thornton, P. Hennings Yeomans, J. Kovačević and B.V.K.V. Kumar, ''Wavelet
packet correlation methods in biometrics'',
Proc. IEEE Int. Conf. Acoust., Speech, and Signal Proc., Philadelphia, PA,
March 2005., pp. II:81-84. |
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