Summary
This paper presents a fast GPU (Graphics Processing Unit) implementation to enhance fingerprint images by a Gabor filter-bank based algorithm. We apply a Gabor filter bank and compute image variances of the convolution responses. We then select parts of these responses and compose the final enhanced image. The algorithm presents a good mapping of data elements and partitions the processing steps into parallel threads to exploit GPU parallelism. The algorithm was implemented on the CPU as well. Both implementations were fed fingerprint images of different sizes and qualities from the FVC2004 DB2 database. We compare the execution speed between the CPU and GPU. This comparison shows that the algorithm is at least 2 times faster on a 112 cores GPU than the CPU.
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Lehtihet, R., El Oraiby, W., Benmohammed, M. (2011). Improved Fingerprint Enhancement Performance via GPU Programming. In: Choraś, R.S. (eds) Image Processing and Communications Challenges 3. Advances in Intelligent and Soft Computing, vol 102. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23154-4_2
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DOI: https://doi.org/10.1007/978-3-642-23154-4_2
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