2026-09-20T17:07:35Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/89206 2023-07-11 col_2142_8888 col_2142_5131 com_2142_8887 com_2142_234 com_2142_5130

Hu, Yih-Chun Adhikari, Anku 2016-03-02T21:06:37Z 2016-03-02T21:06:37Z 2018-03-03T10:15:19Z 2015-11-30 2015-12 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). No. of bitstreams: 2 ADHIKARI-THESIS-2015.pdf: 15946250 bytes, checksum: f7bdb1d4d91ef73f779ed2fc9557cd8e (MD5) LICENSE.txt: 4210 bytes, checksum: a9cf422d15a8a49240123a4f5f597527 (MD5) Previous issue date: 2015-11-30 Embargo set by: Seth Robbins for item 91409 Lift date: 2018-03-02T21:07:27Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system Limited Restriction Lifted for Item 91409 on 2018-03-03T10:15:19Z. application/pdf http://hdl.handle.net/2142/89206 en Copyright 2015 Anku Adhikari security biometric video processing Video-analysis Inference Electrocardiogram (VID-ECG) image processing signal processing Eulerian Video-analysis inference automated ECG (VID-ECG): improving video-based heart rate detection and exposing security risks of ECG-based biometric authentication text text Electrical & Computer Engineering Electrical & Computer Engineering University of Illinois at Urbana-Champaign Thesis M.S.