{"id":11004,"date":"2013-12-04T00:28:42","date_gmt":"2013-12-03T22:28:42","guid":{"rendered":"http:\/\/hgpu.org\/?p=11004"},"modified":"2013-12-04T00:28:42","modified_gmt":"2013-12-03T22:28:42","slug":"fingerprint-grid-enhancement-on-gpu","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=11004","title":{"rendered":"Fingerprint grid enhancement on GPU"},"content":{"rendered":"<p>This paper presents an optimized GPU (Graphics Processing Unit) implementation for fingerprint images enhancement using a Gabor filter-bank based algorithm. Given a batch of fingerprint images, we apply the Gabor filter bank and compute image variances of the convolution responses. We then select parts of these responses and compose the final enhanced batches. The algorithm exploits GPU parallelism by partitioning the data elements on the GPU parallel threads. The implementation was tested on different batch sizes and different image qualities from the FVC2004 DB2 database. We then compare the execution speed between the CPU and GPU. This comparison shows that the algorithm is by order of magnitudes faster on a GPU than the CPU.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This paper presents an optimized GPU (Graphics Processing Unit) implementation for fingerprint images enhancement using a Gabor filter-bank based algorithm. Given a batch of fingerprint images, we apply the Gabor filter bank and compute image variances of the convolution responses. We then select parts of these responses and compose the final enhanced batches. The algorithm [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[36,11,73,89,3],"tags":[1787,776,1782,1791,14,20,1089],"class_list":["post-11004","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-computer-vision","category-nvidia-cuda","category-paper","tag-algorithms","tag-biometrics","tag-computer-science","tag-computer-vision","tag-cuda","tag-nvidia","tag-nvidia-geforce-gtx-560-ti"],"views":2430,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/11004","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/users\/351"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=11004"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/11004\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=11004"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=11004"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=11004"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}