{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/89206"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/89206","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Video-analysis inference automated ECG (VID-ECG): improving video-based heart rate detection and exposing security risks of ECG-based biometric authentication","abstract":"Many recent biometric authentication methods using heart signals in the form of ECG and its components have been proposed to be used as a unique security key for body area networks (BANs) to authenticate individuals and protect privacy and network security. In this thesis we show how compo- nents of information on cardiac activity, heart rate and beat-to-beat heart pulse information can be extracted easily using our video-based non-contact method and expose the vulnerability of such biometric security protocols. We propose a novel method called Video-analysis Inference Automated ECG (VID-ECG) for pulse extraction by facial video processing. Our al- gorithm combines facial region tracking, motion stabilization, filtering and heart beat information extraction methods to allow automated extraction of each pulse from subject facial videos. VID-ECG results show a high level of accuracy and, unlike related methods in this area, VID-ECG does automatic extraction without knowledge of any frequency range. It is also able to han- dle natural motion in subjects. We applied VID-ECG on a wide range of subjects with varied skin tones, and found accuracy to be high, with more than 0.9 cross-correlation with ground truth and error less than 0.085% of average heart rate for each sample. Results have also been compared with a previously proposed video based method for heart rate extraction, and ac- curacy and beat-to-beat correspondence have been shown to be significantly improved, mainly due to the more realistic filtering used and improved mo- tion handling features of VID-ECG. As we are able to obtain many components of cardiac activity such as average heart rate information and close to real-time beat-to-beat informa- tion, we discuss the implication of our results and how VID-ECG exposes the vulnerability of ECG/cardiac data based biometric authentication meth- ods to remote attack using easily obtainable video data from omnipresent commodity cameras around us today in public and private spaces.","abstract_html":"Many recent biometric authentication methods using heart signals in the form of ECG and its components have been proposed to be used as a unique security key for body area networks (BANs) to authenticate individuals and protect privacy and network security. In this thesis we show how compo- nents of information on cardiac activity, heart rate and beat-to-beat heart pulse information can be extracted easily using our video-based non-contact method and expose the vulnerability of such biometric security protocols. We propose a novel method called Video-analysis Inference Automated ECG (VID-ECG) for pulse extraction by facial video processing. Our al- gorithm combines facial region tracking, motion stabilization, filtering and heart beat information extraction methods to allow automated extraction of each pulse from subject facial videos. VID-ECG results show a high level of accuracy and, unlike related methods in this area, VID-ECG does automatic extraction without knowledge of any frequency range. It is also able to han- dle natural motion in subjects. We applied VID-ECG on a wide range of subjects with varied skin tones, and found accuracy to be high, with more than 0.9 cross-correlation with ground truth and error less than 0.085% of average heart rate for each sample. Results have also been compared with a previously proposed video based method for heart rate extraction, and ac- curacy and beat-to-beat correspondence have been shown to be significantly improved, mainly due to the more realistic filtering used and improved mo- tion handling features of VID-ECG. As we are able to obtain many components of cardiac activity such as average heart rate information and close to real-time beat-to-beat informa- tion, we discuss the implication of our results and how VID-ECG exposes the vulnerability of ECG/cardiac data based biometric authentication meth- ods to remote attack using easily obtainable video data from omnipresent commodity cameras around us today in public and private spaces.","abstract_has_math":false,"creators":["Adhikari, Anku"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engineering","degree_department":null,"school":null,"contributors":["Hu, Yih-Chun"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-03-02T21:06:37Z","date_published":"2016-03-02T21:06:37Z","updated_at":"2026-07-22T22:26:32Z","subjects":["security","biometric","video processing","Video-analysis Inference Electrocardiogram (VID-ECG)","image processing","signal processing","Eulerian"],"languages":["en"],"rights":["Copyright 2015 Anku Adhikari"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/89206","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Hu, Yih-Chun"]},{"key":"dc:creator","label":"Author","values":["Adhikari, Anku"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2016-03-02T21:06:37Z","2018-03-03T10:15:19Z","2015-11-30","2015-12"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"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":["security","biometric","video processing","Video-analysis Inference Electrocardiogram (VID-ECG)","image processing","signal processing","Eulerian"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2015 Anku Adhikari"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/89206"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Many recent biometric authentication methods using heart signals in the form of ECG and its components have been proposed to be used as a unique security key for body area networks (BANs) to authenticate individuals and protect privacy and network security. In this thesis we show how compo- nents of information on cardiac activity, heart rate and beat-to-beat heart pulse information can be extracted easily using our video-based non-contact method and expose the vulnerability of such biometric security protocols. We propose a novel method called Video-analysis Inference Automated ECG (VID-ECG) for pulse extraction by facial video processing. Our al- gorithm combines facial region tracking, motion stabilization, filtering and heart beat information extraction methods to allow automated extraction of each pulse from subject facial videos. VID-ECG results show a high level of accuracy and, unlike related methods in this area, VID-ECG does automatic extraction without knowledge of any frequency range. It is also able to han- dle natural motion in subjects. We applied VID-ECG on a wide range of subjects with varied skin tones, and found accuracy to be high, with more than 0.9 cross-correlation with ground truth and error less than 0.085% of average heart rate for each sample. Results have also been compared with a previously proposed video based method for heart rate extraction, and ac- curacy and beat-to-beat correspondence have been shown to be significantly improved, mainly due to the more realistic filtering used and improved mo- tion handling features of VID-ECG. As we are able to obtain many components of cardiac activity such as average heart rate information and close to real-time beat-to-beat informa- tion, we discuss the implication of our results and how VID-ECG exposes the vulnerability of ECG/cardiac data based biometric authentication meth- ods to remote attack using easily obtainable video data from omnipresent commodity cameras around us today in public and private spaces.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2017-12-01","The student, Anku Adhikari, accepted the attached license on 2015-11-25 at 23:27.","The student, Anku Adhikari, submitted this Thesis for approval on 2015-11-25 at 23:28.","This Thesis was approved for publication on 2015-11-30 at 11:23.","DSpace SAF Submission Ingestion Package generated from Vireo submission #8818 on 2016-03-02 at 14:13:16","Made available in DSpace on 2016-03-02T21:06:37Z (GMT). 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In this thesis we show how compo- nents of information on cardiac activity, heart rate and beat-to-beat heart pulse information can be extracted easily using our video-based non-contact method and expose the vulnerability of such biometric security protocols. We propose a novel method called Video-analysis Inference Automated ECG (VID-ECG) for pulse extraction by facial video processing. Our al- gorithm combines facial region tracking, motion stabilization, filtering and heart beat information extraction methods to allow automated extraction of each pulse from subject facial videos. VID-ECG results show a high level of accuracy and, unlike related methods in this area, VID-ECG does automatic extraction without knowledge of any frequency range. It is also able to han- dle natural motion in subjects. We applied VID-ECG on a wide range of subjects with varied skin tones, and found accuracy to be high, with more than 0.9 cross-correlation with ground truth and error less than 0.085% of average heart rate for each sample. Results have also been compared with a previously proposed video based method for heart rate extraction, and ac- curacy and beat-to-beat correspondence have been shown to be significantly improved, mainly due to the more realistic filtering used and improved mo- tion handling features of VID-ECG. As we are able to obtain many components of cardiac activity such as average heart rate information and close to real-time beat-to-beat informa- tion, we discuss the implication of our results and how VID-ECG exposes the vulnerability of ECG/cardiac data based biometric authentication meth- ods to remote attack using easily obtainable video data from omnipresent commodity cameras around us today in public and private spaces.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2017-12-01","The student, Anku Adhikari, accepted the attached license on 2015-11-25 at 23:27.","The student, Anku Adhikari, submitted this Thesis for approval on 2015-11-25 at 23:28.","This Thesis was approved for publication on 2015-11-30 at 11:23.","DSpace SAF Submission Ingestion Package generated from Vireo submission #8818 on 2016-03-02 at 14:13:16","Made available in DSpace on 2016-03-02T21:06:37Z (GMT). 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