{"id":10650,"date":"2013-10-04T23:34:49","date_gmt":"2013-10-04T20:34:49","guid":{"rendered":"http:\/\/hgpu.org\/?p=10650"},"modified":"2013-10-04T23:34:49","modified_gmt":"2013-10-04T20:34:49","slug":"facial-expression-recognition-review","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=10650","title":{"rendered":"Facial Expression Recognition &#8211; Review"},"content":{"rendered":"<p>Expression recognition (happy, sad, disgust, surprise, angry, fear expressions) is application of advanced object detection, pattern recognition and classification task. Facial expression recognition techniques detecting emotion of people&#8217; using their facial expressions. This has found applications in technical fields such as Human-computer-Interaction (HCI) and security monitoring. It generally requires fast processing and decision making. Therefore, it is imperative to develop innovative recognition methods that can detect facial expressions effectively and efficiently. Although humans recognize facial expressions virtually without efforts or delay, reliable expression recognition by machine remains a challenge as of today. To automate recognition of emotional state, machine must be taught to understand facial gestures. This paper focuses on a review of different techniques for face recognition, face detection and emotion recognition are presented.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Expression recognition (happy, sad, disgust, surprise, angry, fear expressions) is application of advanced object detection, pattern recognition and classification task. Facial expression recognition techniques detecting emotion of people&#8217; using their facial expressions. This has found applications in technical fields such as Human-computer-Interaction (HCI) and security monitoring. It generally requires fast processing and decision making. Therefore, [&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":[11,73,89,3],"tags":[1782,1791,14,20,469],"class_list":["post-10650","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-nvidia","tag-pattern-recognition"],"views":2475,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10650","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=10650"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10650\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=10650"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=10650"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=10650"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}