{"id":301,"date":"2022-06-01T12:24:35","date_gmt":"2022-06-01T02:24:35","guid":{"rendered":"http:\/\/www.computer-vision-software.com\/blog\/?p=301"},"modified":"2022-06-01T12:55:39","modified_gmt":"2022-06-01T02:55:39","slug":"face-recognition-2","status":"publish","type":"post","link":"http:\/\/www.computer-vision-software.com\/blog\/2022\/06\/face-recognition-2\/","title":{"rendered":"Face recognition"},"content":{"rendered":"\n<p>The face recognition demo shows person facial feature training via a single photo and subsequent face matching on the live video stream, using VisionLabs\u2019 library integrated onto the <strong>H22 System on a Module<\/strong> (SoM).<\/p>\n\n\n\n<figure class=\"wp-block-embed-youtube wp-block-embed is-type-video is-provider-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<iframe loading=\"lazy\" title=\"Ambarella H22 SoC-based| Face Recognition Demo\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/k7v8mfbmtxo?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<\/div><\/figure>\n\n\n\n<!--more-->\n\n\n\n<p>The H22 SoM, designed in-house, is a power-efficient camera platform for high-resolution video encoding and live video streaming. The core of the SoM platform is an Ambarella H22S85N\u2122 System on a Chip that integrates an advanced image processing pipeline, H.265 (HEVC) and H.264 (AVC) encoders , and a powerful Quad core ARM\u00ae Cortex\u2122-A53 CPU for advanced business logic like computer vision, flight control, WiFi streaming, and other user applications. The H22 SoM is supplied with the <em>SoM SDK<\/em> \u2013 a Linux-based toolchain that allows executing user-level applications on the ARM core. To speed up the development process, there are a series of reference code samples for the SoM SDK.<\/p>\n\n\n\n<p>The demo system implements a face training scenario using a simple\nmobile app. A single photo, captured through a mobile phone and added to the\ndatabase, is quite enough for the NN to learn. Photos with name tags stored in\nthe mobile app are transferred via WiFi onto the SoM to extract face\ndescriptors and carry on with the recognition scenario. Recognition occurs in\nreal-time on faces detected in the camera\u2019s field of view. The markup is straightforward:\nred\nframes and \u201cUnknown\u201d tags\nfor the people that are not found in the database, green frames and a nametag\nfor the people from the database, and grey frames without a tag for the stage\nwhen the person\u2019s face is found but is still being processed by the recognition\nalgorithm.<\/p>\n\n\n\n<p>This basic face\ndetection and tracking algorithm was put together for demo purposes. More\nrobust solutions are to be selected for more practical usage scenarios. One such\nsolution will be in an upcoming post.<\/p>\n\n\n\n<p>Despite being only a demo implementation, the high resolution and decent image quality enables precise face detection and recognition with indoor lighting in overcrowded conditions. Face recognition capabilities could be a value-added feature for security applications such as seamless entry control. <\/p>\n","protected":false},"excerpt":{"rendered":"<p>The face recognition demo shows person facial feature training via a single photo and subsequent face matching on the live video stream, using VisionLabs\u2019 library integrated onto the H22 System on a Module (SoM).<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[56,39,38,73,35],"tags":[87,57],"class_list":["post-301","post","type-post","status-publish","format-standard","hentry","category-demo","category-demo-video","category-demo-videos","category-face-recognition","category-youtube","tag-face-recognition","tag-machine-learning"],"_links":{"self":[{"href":"http:\/\/www.computer-vision-software.com\/blog\/wp-json\/wp\/v2\/posts\/301","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/www.computer-vision-software.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/www.computer-vision-software.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/www.computer-vision-software.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/www.computer-vision-software.com\/blog\/wp-json\/wp\/v2\/comments?post=301"}],"version-history":[{"count":0,"href":"http:\/\/www.computer-vision-software.com\/blog\/wp-json\/wp\/v2\/posts\/301\/revisions"}],"wp:attachment":[{"href":"http:\/\/www.computer-vision-software.com\/blog\/wp-json\/wp\/v2\/media?parent=301"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.computer-vision-software.com\/blog\/wp-json\/wp\/v2\/categories?post=301"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.computer-vision-software.com\/blog\/wp-json\/wp\/v2\/tags?post=301"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}