{"id":151207,"date":"1998-01-01T00:00:00","date_gmt":"1998-01-01T00:00:00","guid":{"rendered":"https:\/\/new-cm-edgedigital.pages.dev\/en-us\/research\/msr-research-item\/face-tracking-using-robust-statistical-estimation\/"},"modified":"2018-10-16T20:22:57","modified_gmt":"2018-10-17T03:22:57","slug":"face-tracking-using-robust-statistical-estimation","status":"publish","type":"msr-research-item","link":"https:\/\/new-cm-edgedigital.pages.dev\/en-us\/research\/publication\/face-tracking-using-robust-statistical-estimation\/","title":{"rendered":"Face Tracking Using Robust Statistical Estimation"},"content":{"rendered":"<div class=\"asset-content\">\n<p>This paper presents a statistics-based method for estimating the position and size of a face in complex background. Face position and size are estimated based on robust statistical measurements which are derived from two one-dimensional histograms obtained by projecting the result of skin color filtering. The proposed algorithm also utilizes a linear Kalman filter and a simple nonlinear filter to perform smooth tracking and remove jitter. The algorithm has been implemented and tested under a wide range of real-world conditions. It has consistently provided performance which satisfies the following requirements: 1)able to automatically determine the initial position and size of a face and track it in complex background; 2)insensitive to partial occlusions and shadows; 3)insensitive to face orientation and scale changes; 4)insensitive to lighting condition changes; and 5)computationally simple enough to be executed in real-time.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>This paper presents a statistics-based method for estimating the position and size of a face in complex background. Face position and size are estimated based on robust statistical measurements which are derived from two one-dimensional histograms obtained by projecting the result of skin color filtering. The proposed algorithm also utilizes a linear Kalman filter and [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"","msr_doi":"","msr_arxiv_id":"","msr_s2_paper_id":"","msr_mag_id":"","msr_pubmed_id":"","msr_other_authors":"Kristine E. Matthews, M. Ibrahim Sezan, Richard J. 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