Partial Palmprint Matching Using Invariant Local Minutiae Descriptors
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In forensic investigations, it is common for forensic investigators to obtain a photograph of evidence left at the scene of crimes to aid them catch the culprit(s). Although, fingerprints are the most popular evidence that can be used, scene of crime officers claim that more than 30% of the evidence recovered from crime scenes originate from palms. Usually, palmprints evidence left at crime scenes are partial since very rarely full palmprints are obtained. In particular, partial palmprints do not exhibit a structured shape and often do not contain a reference point that can be used for their alignment to achieve efficient matching. This makes conventional matching methods based on alignment and minutiae pairing, as used in fingerprint recognition, to fail in partial palmprint recognition problems. In this paper a new partial-to-full palmprint recognition based on invariant minutiae descriptors is proposed where the partial palmprint’s minutiae are extracted and considered as the distinctive and discriminating features for each palmprint image. This is achieved by assigning to each minutiae a feature descriptor formed using the values of all the orientation histograms of the minutiae at hand. This allows for the descriptors to be rotation invariant and as such do not require any image alignment at the matching stage. The results obtained show that the proposed technique yields a recognition rate of 99.2%. The solution does give a high confidence to the judicial jury in their deliberations and decision.
KeywordsMinutiae Descriptor Orientation Histogram Partial Palmprint
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- 5.Jea, T.Y.: Minutiae-based partial fingerprint recognition. PhD thesis, Buffalo, NY, USA, Adviser-Venugopal Govindaraju (2005)Google Scholar
- 7.Dewan, S.: Elementary, watson: Scan a palm, find a clue (2003), http://www.nytimes.com/
- 9.Lowe, D.G.: Object recognition from local scale-invariant features. In: Proceedings of the International Conference on Computer Vision, vol. 2, p. 1150 (1999)Google Scholar
- 11.Mortensen, E.N., Deng, H., Shapiro, L.: A sift descriptor with global context. In: Proceedings of the 2005 IEEE Conference on Computer Vision and Pattern Recognition, vol. 1 (2005)Google Scholar
- 13.Jain, A., Feng, J.: Latent palmprint matching. To Appear on IEEE Transactions on Pattern Analysis and Machine Intelligence (2009)Google Scholar
- 14.Ichi Funada, J., Ohta, N., Mizoguchi, M., Temma, T., Nakanishi, K., Murai, A., Sugiuchi, T., Wakabayashi, T., Yamada, Y.: Feature extraction method for palmprint considering elimination of creases. In: Proceedings of the 14th International Conference on Pattern Recognition, p. 1849 (1998)Google Scholar
- 16.Amengual, J.C., Juan, A., Prez, J.C., Sez, S., Vilar, J.M.: Real-time minutiae extraction in fingerprint images. In: Proceedings of the 6th IEE International Conference on Image Processing and its Applications (1997)Google Scholar
- 17.Farina, A., Kovács-Vajna, Z.M., Leone, A.: Fingerprint minutiae extraction from skeletonized binary images. Pattern Recognition Lettre 32 (1999)Google Scholar
- 18.Zhao, F., Tang, X.: Preprocessing and postprocessing for skeleton-based fingerprint minutiae extraction. Pattern Recognition Lettre 40 (2007)Google Scholar
- 19.Bicego, M., Lagorio, A., Grosso, E., Tistarelli, M.: On the use of sift features for face authentication. In: Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop, p. 35 (2006)Google Scholar
- 20.Wang, K., Ren, Z., Xiong, X.: Combination of wavelet and sift features for image classification using trained gaussion mixture model. In: Proceedings of the 2008 International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 79–82 (2008)Google Scholar