{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/95341"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/95341","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Real-time 3D face localization and verification","abstract":"We present a method for real-time 3D face localization and verification using a consumer-grade depth camera. Our approach consists of three parts: face detection, head pose estimation, and face verification. Face detection is performed using a standard detection framework which we significantly improve by leveraging depth information. To estimate the pose of the detected face, we developed a technique that uses a combination of the particle swarm optimization (PSO) and the iterative closest point (ICP) algorithm to accurately align a 3D face model to the measured depth data. With the face localized within the image, we can compare a database 3D face model to the depth image to verify the identity of the subject. We learn a similarity metric using a random decision forest to accurately authenticate the subject. We demonstrate state-of-the-art results for both face localization and face verification on standard datasets. Since the camera and our method operate at video rate, our system is capable of continuously authenticating a subject while he/she uses his/her device.","abstract_html":"We present a method for real-time 3D face localization and verification using a consumer-grade depth camera. Our approach consists of three parts: face detection, head pose estimation, and face verification. Face detection is performed using a standard detection framework which we significantly improve by leveraging depth information. To estimate the pose of the detected face, we developed a technique that uses a combination of the particle swarm optimization (PSO) and the iterative closest point (ICP) algorithm to accurately align a 3D face model to the measured depth data. With the face localized within the image, we can compare a database 3D face model to the depth image to verify the identity of the subject. We learn a similarity metric using a random decision forest to accurately authenticate the subject. We demonstrate state-of-the-art results for both face localization and face verification on standard datasets. Since the camera and our method operate at video rate, our system is capable of continuously authenticating a subject while he/she uses his/her device.","abstract_has_math":false,"creators":["Meyer, Gregory Paul"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Do, Minh N.","Patel, Sanjay J.","Hart, John C.","Hoiem, Derek"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-03-01T15:49:05Z","date_published":"2017-03-01T15:49:05Z","updated_at":"2026-07-22T22:26:37Z","subjects":["Computer Vision","3D","Face Detection","Face Verification","Face Recognition","Head Pose Estimation","Depth Cameras"],"languages":["en"],"rights":["Copyright 2016 Gregory Meyer"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/95341","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Do, Minh N.","Patel, Sanjay J.","Hart, John C.","Hoiem, Derek"]},{"key":"dc:creator","label":"Author","values":["Meyer, Gregory Paul"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2017-03-01T15:49:05Z","2016-11-22","2016-12"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Computer Vision","3D","Face Detection","Face Verification","Face Recognition","Head Pose Estimation","Depth Cameras"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2016 Gregory Meyer"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/95341"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["We present a method for real-time 3D face localization and verification using a consumer-grade depth camera. 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Since the camera and our method operate at video rate, our system is capable of continuously authenticating a subject while he/she uses his/her device.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2017-02-28 without embargo terms","The student, Gregory Meyer, accepted the attached license on 2016-11-22 at 15:27.","The student, Gregory Meyer, submitted this Dissertation for approval on 2016-11-22 at 15:42.","This Dissertation was approved for publication on 2016-11-22 at 16:22.","DSpace SAF Submission Ingestion Package generated from Vireo submission #10301 on 2017-02-28 at 14:52:04","Made available in DSpace on 2017-03-01T15:49:05Z (GMT). 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