Abstract
We propose a new fingerprint classification method based on a feature map consisting of orientation and inter-ridge spacing for the latent fingerprint retrieval within the large-scale databases. It is designed for the continuous classification methodology. This method captures unique characteristics for each fingerprint from the distribution of combined features of orientation and inter-ridge spacing of local area. The merit of the proposed approach is that it has translation invariant property and is rubust againt registration error since it is not necessary to locate the core position. Our experiments show that the performance of the proposed approach is comparable to the MASK method, and when it is combined with other classifier i.e. PCASYS, the result classifier outperformes any single classifier previously proposed. Moreover, it can be implemented in the low cost hardware such as embedded fingerprint system since the new algorithm saves the processing time.
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© 2005 Springer-Verlag Berlin Heidelberg
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Lee, SO., Kim, YG., Park, GT. (2005). A Feature Map Consisting of Orientation and Inter-ridge Spacing for Fingerprint Retrieval. In: Kanade, T., Jain, A., Ratha, N.K. (eds) Audio- and Video-Based Biometric Person Authentication. AVBPA 2005. Lecture Notes in Computer Science, vol 3546. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527923_19
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DOI: https://doi.org/10.1007/11527923_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27887-0
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