{"id":3757,"date":"2011-04-30T19:47:31","date_gmt":"2011-04-30T19:47:31","guid":{"rendered":"http:\/\/hgpu.org\/?p=3757"},"modified":"2011-04-30T19:47:31","modified_gmt":"2011-04-30T19:47:31","slug":"gpu-based-partially-connected-neural-evolutionary-network-and-its-application-on-gender-recognition-with-face-images","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=3757","title":{"rendered":"GPU based Partially Connected Neural Evolutionary network and its application on gender recognition with face images"},"content":{"rendered":"<p>An algorithm for evolving neural network via the genetic algorithm based on GPU parallel architecture was implemented on the CUDA, resulting in a system called CuParcone (CUDA based Partially Connected Neural Evolutionary) and was used on gender face recognition. By using the powerful ability of GPU parallel computing, CuParcone achieves a performance increase about 323 times than Parcone algorithm, which runs on a single-processor. With this new model, a gender recognition experiment was made on 530 face images (265 females and 265 males from Color FERET database), including not only frontal faces but also the faces rotated from -40 degrees ~40 degrees in the direction of horizontal, and achieved the accuracy rate of 90.84%.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>An algorithm for evolving neural network via the genetic algorithm based on GPU parallel architecture was implemented on the CUDA, resulting in a system called CuParcone (CUDA based Partially Connected Neural Evolutionary) and was used on gender face recognition. By using the powerful ability of GPU parallel computing, CuParcone achieves a performance increase about 323 [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"open","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":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[11,73,89,3],"tags":[1782,1791,14,901,34,20],"class_list":["post-3757","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-computer-vision","category-nvidia-cuda","category-paper","tag-computer-science","tag-computer-vision","tag-cuda","tag-image-recognition","tag-neural-networks","tag-nvidia"],"views":1949,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3757","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=3757"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3757\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3757"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3757"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3757"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}