{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T05:47:36Z","timestamp":1726120056420},"publisher-location":"Singapore","reference-count":38,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789811548246"},{"type":"electronic","value":"9789811548253"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-981-15-4825-3_17","type":"book-chapter","created":{"date-parts":[[2020,4,25]],"date-time":"2020-04-25T08:02:53Z","timestamp":1587801773000},"page":"213-224","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Gotcha-I: A Multiview Human Videos Dataset"],"prefix":"10.1007","author":[{"given":"Paola","family":"Barra","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carmen","family":"Bisogni","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michele","family":"Nappi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David","family":"Freire-Obreg\u00f3n","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Modesto","family":"Castrill\u00f3n-Santana","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,4,26]]},"reference":[{"key":"17_CR1","unstructured":"Gotcha-I dataset. https:\/\/gotchaproject.github.io\/"},{"key":"17_CR2","doi-asserted-by":"publisher","unstructured":"W\u0142odarczyk, M., Kacperski, D., Sankowski, W., Grabowski, K.: COMPACT: biometric dataset of face images acquired in uncontrolled indoor environment. Comput. Sci. 20(1) (2018). https:\/\/doi.org\/10.7494\/csci.2019.20.1.3020","DOI":"10.7494\/csci.2019.20.1.3020"},{"key":"17_CR3","doi-asserted-by":"crossref","unstructured":"Raposo, R., Hoyle, E., Peixinho, A., Proen\u00e7a, H.: UBEAR: a dataset of ear images captured on-the-move in uncontrolled conditions. In: 2011 IEEE Workshop on Computational Intelligence in Biometrics and Identity Management (SSCI 2011 CIBIM), Paris, France, 11\u201315 April, pp. 84\u201390 (2011)","DOI":"10.1109\/CIBIM.2011.5949208"},{"issue":"4","key":"17_CR4","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1049\/iet-bmt.2016.0178","volume":"7","author":"J Neves","year":"2018","unstructured":"Neves, J., Moreno, J., Proen\u00e7a, H.: QUIS-CAMPI: an annotated multi-biometrics data feed from surveillance scenarios. IET Biom. 7(4), 7 (2018). https:\/\/doi.org\/10.1049\/iet-bmt.2016.0178","journal-title":"IET Biom."},{"key":"17_CR5","doi-asserted-by":"publisher","unstructured":"Hsu, H.J., Chen, K.T.: DroneFace: an open dataset for drone research. In: Proceedings of the 8th ACM on Multimedia Systems Conference (MMSys 2017), pp. 187\u2013192. ACM, New York (2017). https:\/\/doi.org\/10.1145\/3083187.3083214","DOI":"10.1145\/3083187.3083214"},{"key":"17_CR6","doi-asserted-by":"crossref","unstructured":"Di Maio, L., Distasi, R., Nappi, M.: MUBIDUS-I: a multibiometric and multipurpose dataset. In: SITIS 2019 - The 15h International Conference on Signal Image Technology and Internet Based Systems, 26\u201329 November 2019, Sorrento, Italy (2019)","DOI":"10.1109\/SITIS.2019.00124"},{"issue":"8","key":"17_CR7","doi-asserted-by":"publisher","first-page":"1707","DOI":"10.1109\/TPAMI.2015.2496269","volume":"38","author":"X Alameda-Pineda","year":"2015","unstructured":"Alameda-Pineda, X., et al.: SALSA: a novel dataset for multimodal group behavior analysis. IEEE Trans. Pattern Anal. Mach. Intell. 38(8), 1707\u20131720 (2015)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"17_CR8","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1007\/s11263-012-0549-0","volume":"101","author":"G Fanelli","year":"2013","unstructured":"Fanelli, G., Dantone, M., Gall, J., Fossati, A., Van Gool, L.: Random forests for real time 3D face analysis. Int. J. Comput. Vision 101, 437\u2013458 (2013)","journal-title":"Int. J. Comput. Vision"},{"issue":"1","key":"17_CR9","doi-asserted-by":"publisher","first-page":"101415","DOI":"10.1016\/j.giq.2019.101415","volume":"37","author":"DE Bromberg","year":"2019","unstructured":"Bromberg, D.E., Charbonneau, \u00c9., Smith, A.: Public support for facial recognition via police body-worn cameras: findings from a list experiment. Gov. Inf. Q. 37(1), 101415 (2019)","journal-title":"Gov. Inf. Q."},{"key":"17_CR10","doi-asserted-by":"publisher","unstructured":"Younis, O., Al-Nuaimy, W., Rowe, F., Alomari, M.H.: Real-time detection of wearable camera motion using optical flow. In: 2018 IEEE Congress on Evolutionary Computation (CEC). https:\/\/doi.org\/10.1109\/CEC.2018.8477783","DOI":"10.1109\/CEC.2018.8477783"},{"issue":"4","key":"17_CR11","doi-asserted-by":"publisher","first-page":"1002","DOI":"10.1109\/TPAMI.2017.2700390","volume":"40","author":"C Ding","year":"2018","unstructured":"Ding, C., Tao, D.: Trunk-branch ensemble convolutional neural networks for video-based face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 1002\u20131014 (2018). https:\/\/doi.org\/10.1109\/TPAMI.2017.2700390","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"17_CR12","doi-asserted-by":"publisher","first-page":"102805","DOI":"10.1016\/j.cviu.2019.102805","volume":"189","author":"G Guo","year":"2019","unstructured":"Guo, G., Zhang, N.: A survey on deep learning based face recognition. Comput. Vis. Image Underst. 189, 102805 (2019)","journal-title":"Comput. Vis. Image Underst."},{"issue":"4","key":"17_CR13","doi-asserted-by":"publisher","first-page":"1002","DOI":"10.1109\/TPAMI.2017.2700390","volume":"40","author":"G Yue","year":"2018","unstructured":"Yue, G., Lu, L.: Face recognition based on histogram equalization and convolution neural network. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 1002\u20131014 (2018). https:\/\/doi.org\/10.1109\/TPAMI.2017.2700390","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"17_CR14","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1109\/TPAMI.2017.2781233","volume":"41","author":"R Ranjan","year":"2019","unstructured":"Ranjan, R., Patel, V.M., Chellappa, R.: Hyperface: a deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition. IEEE Trans. Pattern Anal. Mach. Intell. 41, 121\u2013135 (2019)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"17_CR15","doi-asserted-by":"publisher","first-page":"102805","DOI":"10.1016\/j.cviu.2019.102805","volume":"189","author":"P Barra","year":"2019","unstructured":"Barra, P., Bisogni, C., Nappi, M., Ricciardi, S.: A survey on deep learning based face recognition. Computer Vis. Image Underst. 189, 102805 (2019)","journal-title":"Computer Vis. Image Underst."},{"key":"17_CR16","doi-asserted-by":"publisher","first-page":"64256","DOI":"10.1109\/ACCESS.2019.2917451","volume":"7","author":"AF Abate","year":"2019","unstructured":"Abate, A.F., Barra, P., Bisogni, C., Nappi, M., Ricciardi, S.: Near real-time three axis head pose estimation without training. IEEE Access 7, 64256\u201364265 (2019)","journal-title":"IEEE Access"},{"key":"17_CR17","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1016\/j.patrec.2018.10.009","volume":"130","author":"DP Chowdhury","year":"2018","unstructured":"Chowdhury, D.P., Bakshi, S., Sa, P.K., Majhi, B.: Wavelet energy feature based source camera identification for ear biometric images. Pattern Recogn. Lett. 130, 139\u2013147 (2018)","journal-title":"Pattern Recogn. Lett."},{"key":"17_CR18","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1016\/j.patrec.2017.11.012","volume":"101","author":"EG Llano","year":"2018","unstructured":"Llano, E.G., V\u00e1zquez, M.S.G., Vargas, J.M.C., Fuentes, L.M.Z., Acosta, A.A.R.: Optimized robust multi-sensor scheme for simultaneous video and image iris recognition. Pattern Recogn. Lett. 101, 44\u201351 (2018)","journal-title":"Pattern Recogn. Lett."},{"key":"17_CR19","doi-asserted-by":"publisher","unstructured":"Sonal, Singh, A.: Review on multibiometrics: classifications, normalization and fusion levels. In: 2018 International Conference on Advances in Computing and Communication Engineering (ICACCE), 22\u201323 June 2018. IEEE (2018). https:\/\/doi.org\/10.1109\/ICACCE.2018.8441727","DOI":"10.1109\/ICACCE.2018.8441727"},{"key":"17_CR20","doi-asserted-by":"publisher","unstructured":"Bisogni, C., Nappi, M.: Multibiometric score-level fusion through optimization and training. In: 2019 3rd International Conference on Bio-engineering for Smart Technologies (BioSMART). 24\u201326 April 2019. IEEE (2019). https:\/\/doi.org\/10.1109\/BIOSMART.2019.8734162","DOI":"10.1109\/BIOSMART.2019.8734162"},{"key":"17_CR21","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1007\/978-981-15-1301-5_14","volume-title":"Smart City and Informatization","author":"AF Abate","year":"2019","unstructured":"Abate, A.F., Bisogni, C., Castiglione, A., Distasi, R., Petrosino, A.: Optimization of score-level biometric data fusion by constraint construction training. In: Wang, G., El Saddik, A., Lai, X., Martinez Perez, G., Choo, K.-K.R. (eds.) iSCI 2019. CCIS, vol. 1122, pp. 167\u2013179. Springer, Singapore (2019). https:\/\/doi.org\/10.1007\/978-981-15-1301-5_14"},{"key":"17_CR22","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1016\/j.procs.2018.05.053","volume":"132","author":"A Dhomne","year":"2018","unstructured":"Dhomne, A., Kumar, R., Bhan, V.: Gender recognition through face using deep learning. Proc. Comput. Sci. 132, 2\u201310 (2018)","journal-title":"Proc. Comput. Sci."},{"key":"17_CR23","doi-asserted-by":"publisher","unstructured":"Cerkezi, L., Topal, C.: Gender recognition with uniform local binary patterns. In: 2018 26th Signal Processing and Communications Applications Conference (SIU) (2018). https:\/\/doi.org\/10.1109\/SIU.2018.8404587","DOI":"10.1109\/SIU.2018.8404587"},{"key":"17_CR24","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.patrec.2018.04.020","volume":"126","author":"ER Isaac","year":"2019","unstructured":"Isaac, E.R., Elias, S., Rajagopalan, S., Easwarakumar, K.S.: Multiview gait-based gender classification through pose-based voting. Pattern Recogn. Lett. 126, 41\u201350 (2019)","journal-title":"Pattern Recogn. Lett."},{"key":"17_CR25","doi-asserted-by":"publisher","unstructured":"Barra, P., Bisogni, C., Nappi, M., Freire Obregon, D., Castrillon-Santana, M.: Gender classification on 2D human skeleton. In: 3rd International Conference on Bio-Engineering for Smart Technologies (BioSMART 2019) (2019). https:\/\/doi.org\/10.1109\/BIOSMART.2019.8734198","DOI":"10.1109\/BIOSMART.2019.8734198"},{"issue":"1","key":"17_CR26","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1016\/j.eswa.2017.10.017","volume":"93","author":"A Jain","year":"2018","unstructured":"Jain, A., Kanhangad, V.: Gender classification in smartphones using gait information. Exp. Syst. Appl. 93(1), 257\u2013266 (2018)","journal-title":"Exp. Syst. Appl."},{"key":"17_CR27","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1007\/978-981-15-1304-6_15","volume-title":"Dependability in Sensor, Cloud, and Big Data Systems and Applications","author":"P Barra","year":"2019","unstructured":"Barra, P., Bisogni, C., Nappi, M., Freire-Obreg\u00f3n, D., Castrill\u00f3n-Santana, M.: Gait analysis for gender classification in forensics. In: Wang, G., Bhuiyan, M.Z.A., De Capitani di Vimercati, S., Ren, Y. (eds.) DependSys 2019. CCIS, vol. 1123, pp. 180\u2013190. Springer, Singapore (2019). https:\/\/doi.org\/10.1007\/978-981-15-1304-6_15"},{"key":"17_CR28","unstructured":"Liu, Z., Luo, P., Wang, X., Tang, X.: Large-scale celebfaces attributes (CelebA) dataset. The Chinese University of Hong Kong, Multimedia Laboratory (2015)"},{"issue":"3","key":"17_CR29","doi-asserted-by":"publisher","first-page":"255","DOI":"10.3103\/S8756699019030075","volume":"55","author":"DV Pakulich","year":"2019","unstructured":"Pakulich, D.V., Yakimov, S.A., Alyamkin, S.A.: Age recognition from facial images using convolutional neural networks. Optoelectron. Instrument. Data Process. 55(3), 255\u2013262 (2019). https:\/\/doi.org\/10.3103\/S8756699019030075","journal-title":"Optoelectron. Instrument. Data Process."},{"issue":"11","key":"17_CR30","doi-asserted-by":"publisher","first-page":"2505","DOI":"10.1109\/TIFS.2017.2695456","volume":"12","author":"MTB Iqbal","year":"2017","unstructured":"Iqbal, M.T.B., Shoyaib, M., Ryu, B., Abdullah-Al-Wadud, M., Chae, O.: Directional age-primitive pattern (DAPP) for human age group recognition and age estimation. IEEE Trans. Inf. Forensics Secur. 12(11), 2505\u20132517 (2017)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"17_CR31","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1016\/j.patcog.2017.06.028","volume":"72","author":"P Rodr\u00edguez","year":"2017","unstructured":"Rodr\u00edguez, P., Cucurull, G., Gonfaus, J.M., Roca, F.X., Gonzalez, J.: Age and gender recognition in the wild with deep attention. Pattern Recogn. 72, 563\u2013571 (2017)","journal-title":"Pattern Recogn."},{"key":"17_CR32","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1007\/978-3-030-17798-0_27","volume-title":"Advances in Computer Vision","author":"S-Y Wen","year":"2020","unstructured":"Wen, S.-Y., Yen, Y., Chen, A.Y.: Human tracking for facility surveillance. In: Arai, K., Kapoor, S. (eds.) CVC 2019. AISC, vol. 944, pp. 329\u2013338. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-17798-0_27"},{"issue":"2","key":"17_CR33","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1007\/s40745-017-0123-2","volume":"5","author":"HS Dadi","year":"2017","unstructured":"Dadi, H.S., Pillutla, G.K.M., Makkena, M.L.: Face recognition and human tracking using GMM, HOG and SVM in surveillance videos. Ann. Data Sci. 5(2), 157\u2013179 (2017). https:\/\/doi.org\/10.1007\/s40745-017-0123-2","journal-title":"Ann. Data Sci."},{"issue":"3","key":"17_CR34","doi-asserted-by":"publisher","first-page":"470","DOI":"10.1109\/TCSVT.2016.2637818","volume":"27","author":"YG Lee","year":"2017","unstructured":"Lee, Y.G., Chen, S.C., Hwang, J.N., Hung, Y.P.: An ensemble of invariant features for person reidentification. IEEE Trans. Circ. Syst. Video Technol. 27(3), 470\u2013483 (2017)","journal-title":"IEEE Trans. Circ. Syst. Video Technol."},{"key":"17_CR35","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1007\/978-981-15-1301-5_9","volume-title":"Smart City and Informatization","author":"L Anzalone","year":"2019","unstructured":"Anzalone, L., Barra, P., Barra, S., Narducci, F., Nappi, M.: Transfer learning for facial attributes prediction and clustering. In: Wang, G., El Saddik, A., Lai, X., Martinez Perez, G., Choo, K.-K.R. (eds.) iSCI 2019. CCIS, vol. 1122, pp. 105\u2013117. Springer, Singapore (2019). https:\/\/doi.org\/10.1007\/978-981-15-1301-5_9"},{"key":"17_CR36","doi-asserted-by":"publisher","unstructured":"Wang, P., Su, F., Zhao, Z.: Joint multi-feature fusion and attribute relationships for facial attribute prediction. 2017 IEEE Visual Communications and Image Processing (VCIP) (2017). https:\/\/doi.org\/10.1109\/VCIP.2017.8305036","DOI":"10.1109\/VCIP.2017.8305036"},{"key":"17_CR37","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1016\/j.patcog.2018.03.018","volume":"80","author":"N Zhuang","year":"2018","unstructured":"Zhuang, N., Yan, Y., Chen, S., Wang, H., Shen, C.: Multi-label learning based deep transfer neural network for facial attribute classification. Pattern Recogn. 80, 225\u2013240 (2018)","journal-title":"Pattern Recogn."},{"key":"17_CR38","doi-asserted-by":"crossref","unstructured":"Cao, Z., Simon, T., Wei, S.E., Sheikh, Y.: OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields (2018)","DOI":"10.1109\/CVPR.2017.143"}],"container-title":["Communications in Computer and Information Science","Security in Computing and Communications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-15-4825-3_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,3,11]],"date-time":"2021-03-11T00:34:26Z","timestamp":1615422866000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-981-15-4825-3_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9789811548246","9789811548253"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-981-15-4825-3_17","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"26 April 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SSCC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Security in Computing and Communication","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Trivandrum","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 December 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 December 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"sscc2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.acn-conference.org\/sscc2019\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EDAS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"61","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":"22","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":"7","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":"36% - 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":"3.2","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":"3","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)"}}]}}