{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T22:23:10Z","timestamp":1743114190731,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":41,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789811904677"},{"type":"electronic","value":"9789811904684"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-981-19-0468-4_25","type":"book-chapter","created":{"date-parts":[[2022,2,25]],"date-time":"2022-02-25T14:02:47Z","timestamp":1645797767000},"page":"336-350","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Fine-Tuning Strategy Based on Real Scenes in Gait Identification"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6808-9721","authenticated-orcid":false,"given":"Xianggang","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Jing","family":"Zeng","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7314-3684","authenticated-orcid":false,"given":"Guoyu","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,2,26]]},"reference":[{"key":"25_CR1","doi-asserted-by":"crossref","unstructured":"Ngo, T.T., Makihara, Y., Nagahara, H., Mukaigawa, Y., Yagi, Y.: Orientation-compensative signal registration for owner authentication using an accelerometer. lEICE Trans. Inf. Syst. 97(3), 541\u2013553 (2016)","DOI":"10.1587\/transinf.E97.D.541"},{"key":"25_CR2","doi-asserted-by":"crossref","unstructured":"Zhong, Y., Deng, Y.: Sensor orientation invariant mobile gait biometrics. In: International Joint Conference on Biometrics, IJCB (2014)","DOI":"10.1109\/BTAS.2014.6996246"},{"key":"25_CR3","doi-asserted-by":"crossref","unstructured":"Subramanian, R., et al.: Orientation invariant gait matching algorithm based on the Kabsch alignment. In: IEEE International Conference on Identity, Security and Behavior Analysis (ISBA 2015), pp. 1\u20138 (2015)","DOI":"10.1109\/ISBA.2015.7126347"},{"key":"25_CR4","doi-asserted-by":"crossref","unstructured":"Sprager, S., Juric, M.B: Inertial sensor-based gait recognition: a review. Sensors 15(9), 22089\u201322127 (2015)","DOI":"10.3390\/s150922089"},{"key":"25_CR5","doi-asserted-by":"crossref","unstructured":"Johnston, A.H., Weiss, G.M.: Smartwatch-based biometric gait recognition. In: 2015 IEEE 7th International Conference on Biometrics Theory, Applications and Systems (BTAS), pp. 1\u20136 (2015)","DOI":"10.1109\/BTAS.2015.7358794"},{"key":"25_CR6","doi-asserted-by":"crossref","unstructured":"Gafurov, D., Snekkkenes, E.: Arm swing as a weak biometric for unobtrusive user authentication. In: 2008 International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 1080\u20131087 (2008)","DOI":"10.1109\/IIH-MSP.2008.47"},{"key":"25_CR7","doi-asserted-by":"crossref","unstructured":"San-Segundo, R., Cordoba, R., Ferreiros, J., D\u2019Haro-Enriquez, L.: Frequency features and GMM-UBM approach for gait-based person identification using smartphone inertial signals. Pattern Recogn. Lett. 73(April 1), 60\u201367 (2016)","DOI":"10.1016\/j.patrec.2016.01.008"},{"key":"25_CR8","doi-asserted-by":"crossref","unstructured":"Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"25_CR9","doi-asserted-by":"crossref","unstructured":"Muaaz, M., Mayrhofer, R.: Orientation independent cell phone based gait authentication. In: Proceedings of the 12th International Conference on Advances in Mobile Computing and Multimedia, pp. 161\u2013164 (2014)","DOI":"10.1145\/2684103.2684152"},{"key":"25_CR10","doi-asserted-by":"crossref","unstructured":"Lu, H., Huang, J., Saha, T., Nachman, L.: Unobtrusive gait verification for mobile phones. In: Proceedings of the 2014 ACM International Symposium on Wearable Computers, pp. 91\u201398 (2014)","DOI":"10.1145\/2634317.2642868"},{"issue":"10","key":"25_CR11","doi-asserted-by":"publisher","first-page":"100","DOI":"10.3390\/sym8100100","volume":"8","author":"R Dama\u0161evi\u010dius","year":"2016","unstructured":"Dama\u0161evi\u010dius, R., Maskeli\u016bnas, R., Ven\u010dkauskas, A., Wo\u017aniak, M.: Smartphone user identity verification using gait characteristics. Symmetry 8(10), 100 (2016)","journal-title":"Symmetry"},{"key":"25_CR12","doi-asserted-by":"crossref","unstructured":"Sprager, S., Zazula, D.: Impact of different walking surfaces on gait identification based on higher-order statistics of accelerometer data. In: 2011 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), pp. 360\u2013365 (2011)","DOI":"10.1109\/ICSIPA.2011.6144100"},{"issue":"9","key":"25_CR13","doi-asserted-by":"publisher","first-page":"17037","DOI":"10.3390\/s140917037","volume":"14","author":"B Sun","year":"2014","unstructured":"Sun, B., Wang, Y., Banda, J.: Gait characteristic analysis and identification based on the iPhone\u2019s accelerometer and gyrometer. Sensors 14(9), 17037\u201317054 (2014)","journal-title":"Sensors"},{"issue":"7","key":"25_CR14","doi-asserted-by":"publisher","first-page":"2572","DOI":"10.1016\/j.patcog.2010.01.017","volume":"43","author":"G Trivino","year":"2010","unstructured":"Trivino, G., Alvarez-Alvarez, A., Bailador, G.: Application of the computational theory of perceptions to human gait pattern recognition. Pattern Recogn. 43(7), 2572\u20132581 (2010)","journal-title":"Pattern Recogn."},{"issue":"4","key":"25_CR15","doi-asserted-by":"publisher","first-page":"3822","DOI":"10.3390\/ijerph110403822","volume":"11","author":"B-S Lin","year":"2014","unstructured":"Lin, B.-S., Liu, Y.-T., Yu, C., Jan, G.E., Hsiao, B.-T.: Gait recognition and walking exercise intensity estimation. Int. J. Environ. Res. Public Health 11(4), 3822\u20133844 (2014)","journal-title":"Int. J. Environ. Res. Public Health"},{"issue":"6","key":"25_CR16","doi-asserted-by":"publisher","first-page":"1111","DOI":"10.1088\/0967-3334\/33\/6\/1111","volume":"33","author":"H Sun","year":"2012","unstructured":"Sun, H., Yuao, T.: Curve aligning approach for gait authentication based on a wearable accelerometer. Physiol. Meas. 33(6), 1111 (2012)","journal-title":"Physiol. Meas."},{"key":"25_CR17","doi-asserted-by":"crossref","unstructured":"Primo, A., Phoha, V.V., Kumar, R., Serwadda, A.: Context-aware active authentication using smartphone accelerometer measurements. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 98\u2013105. Springer, New York (2014)","DOI":"10.1109\/CVPRW.2014.20"},{"key":"25_CR18","unstructured":"Mondal, S., Nandy, A., Chakraborty, P., Nandi, G.C.: Gait based personal identification system using rotation sensor. J. Emerg. Trends Comput. Inf. Sci. 3(2), 395\u2013402 (2012)"},{"key":"25_CR19","doi-asserted-by":"crossref","unstructured":"San-Segundo, R., Echeverry-Correa, J.D., Salamea-Palacios, C., Lutfi, S.L., Pardo, J.M.: I-vector analysis for gait-based person identification using smartphone inertial signals. Pervasive Mob. Comput. 38, 140\u2013153 (2017)","DOI":"10.1016\/j.pmcj.2016.09.007"},{"key":"25_CR20","doi-asserted-by":"crossref","unstructured":"Kobayashi, T., Hasida, K., Otsu, N.: Rotation invariant feature extraction from 3-D acceleration signals. In: 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3684\u20133687 (2011)","DOI":"10.1109\/ICASSP.2011.5947150"},{"issue":"11","key":"25_CR21","first-page":"369","volume":"5","author":"S Sprager","year":"2009","unstructured":"Sprager, S., Zazula, D.: A cumulant-based method for gait identification using accelerometer data with principal component analysis and support vector machine. WSEAS Trans. Signal Process. 5(11), 369\u2013378 (2009)","journal-title":"WSEAS Trans. Signal Process."},{"key":"25_CR22","doi-asserted-by":"crossref","unstructured":"Nickel, C., Brandt, H., Busch, C.: Benchmarking the performance of SVMs and HMMs for accelerometer-based biometric gait recognition. In: 2011 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), pp. 281\u2013286 (2011)","DOI":"10.1109\/ISSPIT.2011.6151574"},{"key":"25_CR23","doi-asserted-by":"crossref","unstructured":"Watanabe, Y.: Influence of holding smart phone for acceleration-based gait authentication. In: 2014 Fifth International Conference on Emerging Security Technologies, pp. 30\u201333 (2014)","DOI":"10.1109\/EST.2014.24"},{"issue":"7","key":"25_CR24","doi-asserted-by":"publisher","first-page":"51","DOI":"10.4304\/jcp.1.7.51-59","volume":"1","author":"D Gafurov","year":"2006","unstructured":"Gafurov, D., Helkala, K., S\u00f8ndrol, T.: Biometric gait authentication using accelerometer sensor. J. Comput. 1(7), 51\u201359 (2006)","journal-title":"J. Comput."},{"issue":"3","key":"25_CR25","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1109\/TPAMI.2012.121","volume":"35","author":"J Frank","year":"2012","unstructured":"Frank, J., Mannor, S., Pineau, J., Precup, D.: Time series analysis using geometric template matching. IEEE Trans. Pattern Anal. Mach. Intell. 35(3), 740\u2013754 (2012)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"25_CR26","doi-asserted-by":"crossref","unstructured":"Gafurov, D., Bours, P.: Improved hip-based individual recognition using wearable motion recording sensor. In: Security Technology, Disaster Recovery & Business Continuity-International Conferences, pp. 179\u2013186 (2010)","DOI":"10.1007\/978-3-642-17610-4_20"},{"key":"25_CR27","doi-asserted-by":"crossref","unstructured":"Nickel, C., Busch, C.: Does a cycle-based segmentation improve accelerometer-based biometric gait recognition?. In: 2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA), pp. 746\u2013751 (2012)","DOI":"10.1109\/ISSPA.2012.6310652"},{"issue":"6","key":"25_CR28","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1007\/s10207-015-0273-1","volume":"14","author":"T Hoang","year":"2015","unstructured":"Hoang, T., Choi, D., Nguyen, T.: Gait authentication on mobile phone using biometric cryptosystem and fuzzy commitment scheme. Int. J. Inf. Secur. 14(6), 549\u2013560 (2015). https:\/\/doi.org\/10.1007\/s10207-015-0273-1","journal-title":"Int. J. Inf. Secur."},{"issue":"9","key":"25_CR29","doi-asserted-by":"publisher","first-page":"1961","DOI":"10.1109\/TMC.2014.2365185","volume":"14","author":"Y Ren","year":"2014","unstructured":"Ren, Y., Chen, Y., Chuah, M.C., Yang, J.: User verification leveraging gait recognition for smartphone enabled mobile healthcare systems. IEEE Trans. Mob. Comput. 14(9), 1961\u20131974 (2014)","journal-title":"IEEE Trans. Mob. Comput."},{"key":"25_CR30","doi-asserted-by":"crossref","unstructured":"Sprager, S., Juric, M.B: An efficient HOS-based gait authentication of accelerometer data. IEEE Trans. Inf. Forensics Secur. 10(7), 1486\u20131498 (2015)","DOI":"10.1109\/TIFS.2015.2415753"},{"key":"25_CR31","doi-asserted-by":"crossref","unstructured":"Zeng, Y., Pande, A., Zhu, J., Mohapatra, P.: WearIA: wearable device implicit authentication based on activity information. In: 2017 IEEE 18th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM), pp. 1\u20139 (2017)","DOI":"10.1109\/WoWMoM.2017.7974305"},{"key":"25_CR32","doi-asserted-by":"crossref","unstructured":"Gafurov, D., Snekkenes, E., Bours, P.: Improved gait recognition performance using cycle matching. In: 2010 IEEE 24th International Conference on Advanced Information Networking and Applications Workshops, pp. 836\u2013841 (2010)","DOI":"10.1109\/WAINA.2010.145"},{"key":"25_CR33","doi-asserted-by":"crossref","unstructured":"Ren, Y., Chen, Y., Chuah, M.C., Yang, J.: Smartphone based user verification leveraging gait recognition for mobile healthcare systems. In: 2013 IEEE International Conference on Sensing, Communications and Networking (SECON), pp. 149\u2013157 (2013)","DOI":"10.1109\/SAHCN.2013.6644973"},{"key":"25_CR34","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Pan, G., Jia, K., Lu, M., Wang, Y., Wu, Z.: Accelerometer-based gait recognition by sparse representation of signature points with clusters. IEEE Trans. Cybern. 45(9) 1864\u20131875 (2014)","DOI":"10.1109\/TCYB.2014.2361287"},{"key":"25_CR35","doi-asserted-by":"crossref","unstructured":"Kwapisz, J.R., Weiss, G.M., Moore, S.A.: Cell phone-based biometric identification. In: 2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS), pp. 1\u20137 (2010)","DOI":"10.1109\/BTAS.2010.5634532"},{"key":"25_CR36","doi-asserted-by":"crossref","unstructured":"Sam\u00e0, Al., Ruiz, F.J., Agell, N., P\u00e9rez-L\u00f3pez, C., Catal\u00e0, A., Cabestany, J.: Gait identification by means of box approximation geometry of reconstructed attractors in latent space. Neurocomputing 121(Dec. 9), 79\u201388 (2013)","DOI":"10.1016\/j.neucom.2012.12.042"},{"key":"25_CR37","doi-asserted-by":"crossref","unstructured":"B\u00e4chlin, M., Schumm, J., Roggen, D., T\u00f6ster, G.: Quantifying gait similarity: user authentication and real-world challenge. In: International Conference on Biometrics, pp. 1040\u2013104 (2009)","DOI":"10.1007\/978-3-642-01793-3_105"},{"key":"25_CR38","doi-asserted-by":"crossref","unstructured":"Frank, J., Mannor, S., Precup, D.: Activity and gait recognition with time-delay embeddings. In: AAAI, pp. 1581\u20131586 (2010)","DOI":"10.1609\/aaai.v24i1.7724"},{"key":"25_CR39","doi-asserted-by":"crossref","unstructured":"Preuveneers, D., Joosen, W., et al.: Improving resilience of behaviometric based continuous authentication with multiple accelerometers. In: IFIP Annual Conference on Data and Applications Security and Privacy, pp. 473\u2013485 (2017)","DOI":"10.1007\/978-3-319-61176-1_26"},{"key":"25_CR40","doi-asserted-by":"crossref","unstructured":"Dehzangi, O., Taherisadr, M., ChangalVala, R.: IMU-based gait recognition using convolutional neural networks and multi-sensor fusion. Sensors 17(12), 2735 (2017)","DOI":"10.3390\/s17122735"},{"key":"25_CR41","doi-asserted-by":"crossref","unstructured":"Gadaleta, M., Rossi, M.: IDNet: smartphone-based gait recognition with convolutional neural networks. Pattern Recogn. 74, 25\u201337 (2018)","DOI":"10.1016\/j.patcog.2017.09.005"}],"container-title":["Communications in Computer and Information Science","Ubiquitous Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-19-0468-4_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,27]],"date-time":"2023-01-27T22:44:23Z","timestamp":1674859463000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-19-0468-4_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9789811904677","9789811904684"],"references-count":41,"URL":"https:\/\/doi.org\/10.1007\/978-981-19-0468-4_25","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"26 February 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"UbiSec","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Inernational Conference on Ubiquitous Security","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guangzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 December 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31 December 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ubisec2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/ubisecurity.org\/2021\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"96","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"26","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"27% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}