Abstract
Dynamic Programming (DP) matching has been applied to solve distortion in spectral-based fingerprint recognition. However, spectral data is redundant, and its size is huge. PCA could be used to reduce the data size, but leads to loss of topographical information in projected vectors. This allows only inter-vector similarity estimations such as Euclid or Mahalanobis distances, and proves to be inadequate in presence of distortion occurring in finger sweeping with a line sensor. In this paper, we propose a novel two-step PCA to extract compact eigenfeatures amenable to DP matching. The first PCA extracts eigenfeatures of Fourier spectra from each image line. The second extracts eigenfeatures from all lines to form the feature templates. In matching, the feature templates are inversely transformed to line-by-line representations on the first PCA subspace for DP matching. Fingerprint matching experiments demonstrate the effectiveness of our proposed approach in template size reduction and accuracy improvement.
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References
Asai, K., Kato, Y., Hoshino, Y., Kiji, K.: Automatic fingerprint identification. Proc. Soc. of Photo-Optical Instrumentation Engineers 182, 49–56 (1979)
Ratha, N., Bolle, R. (eds.): Automatic Fingerprint Recognition Systems. Springer, Heidelberg (2004)
Mainguet, J.: Biometrics for large-scale consumer products. In: Proc. Intl. Conf. on Artificial Intelligence, pp. 310–314 (2003)
Matsumoto, N., Sato, S., Fujiyoshi, H., Umezaki, T.: Evaluation of a fingerprint verification method based on LPC analysis. Trans. of IEE 122(5), 799–807 (2002)
Moghaddam, B., Pentland, A.: Probabilistic visual learning for object representation. IEEE Trans. on Pattern Anal. Machine Intell. 19(7), 696–710 (1997)
Kamei, T., Mizoguchi, M.: Fingerprint preselection using eigenfeatures. In: Proc. Intl. Conf. on Computer Vision and Pattern Recognition, pp. 918–923 (1998)
Kamei, T.: Face retieval by an adaptive Mahalanobis distance using a confidence factor. Proc. IEEE Intl. Conf. on Image Processing 1, 153–156 (2002)
Sakoe, H., Chiba, S.: Dynamic programming algorthim optimization for spoken word recognition. Speech and Signal Processing 26(1), 43–49 (1978)
Jain, A., Prabhakar, S., Hong, L.: A multichannel approach to fingerprint classification. IEEE Trans. on Pattern Anal. Machine Intell. 21(4), 348–359 (1999)
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© 2007 Springer-Verlag Berlin Heidelberg
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Danev, B., Kamei, T. (2007). Spectral Eigenfeatures for Effective DP Matching in Fingerprint Recognition. In: Kropatsch, W.G., Kampel, M., Hanbury, A. (eds) Computer Analysis of Images and Patterns. CAIP 2007. Lecture Notes in Computer Science, vol 4673. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74272-2_100
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DOI: https://doi.org/10.1007/978-3-540-74272-2_100
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74271-5
Online ISBN: 978-3-540-74272-2
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