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Hardware fingerprint masker verification#
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In: IEEE International Symposium on Intelligent Signal Processing, WISP (2007) PRIME 2007, 169–172 (2007)įons, F., Fons, M., Canto, E.: Approaching fingerprint image enhancement through reconfigurable hardware accelerators. Geomorphology 130, 162–172 (2011)įons, F., Fons, M., Canto, E., Lopez, M.: Flexible hardware for fingerprint image processing, research in microelectronics and electronics conference. Veldhoven, The Netherlands (2001)ĭragut, L., Eisank, C., Strasser, T.: Local variance for multi-scale analysis in geomorphometry. Workshop on Circuits, Systems and Signal Processing. Wiley, London (2011)īazen, A.M., Gerez, S.H.: Segmentation of Fingerprint Images. Test results show an improved speed for the hardware architecture while sustaining reasonable enhancement benchmarks.īailey, D.G.: Design for Embedded Image Processing on FPGAs. On the way to achieve real-time hardware implementation, certain important computationally efficient approximations are deployed. A modified local normalization has been proposed along with its efficient hardware structure.
In essence, its task is to provide the job of foreground segmentation. To counter the background noise amplification, the research work presented here introduces a correction factor that, once multiplied with the output of the conventional normalization algorithm, will enhance only the feature region of the image while avoiding the background area entirely. Conventional local normalization techniques involve pixelwise division by the local variance and thus have the potential to amplify unwanted noise structures, especially in low-activity background regions. Local normalization techniques are employed, which are a better alternative to deal with local image statistics. Global techniques do not produce satisfying and definitive results for fingerprint image normalization due to the non-stationary nature of the image contents.