{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T01:07:57Z","timestamp":1743124077483,"version":"3.40.3"},"publisher-location":"Cham","reference-count":42,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031216473"},{"type":"electronic","value":"9783031216480"}],"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\/978-3-031-21648-0_6","type":"book-chapter","created":{"date-parts":[[2022,11,25]],"date-time":"2022-11-25T00:05:14Z","timestamp":1669334714000},"page":"83-97","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Impact of\u00a0Type of\u00a0Convolution Operation on\u00a0Performance of\u00a0Convolutional Neural Networks for\u00a0Online Signature Verification"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8490-3594","authenticated-orcid":false,"given":"Chandra Sekhar","family":"Vorugunti","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8947-4840","authenticated-orcid":false,"given":"Balasubramanian","family":"Subramanian","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8339-1179","authenticated-orcid":false,"given":"Avinash","family":"Gautam","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5953-6246","authenticated-orcid":false,"given":"Viswanath","family":"Pulabaigari","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,25]]},"reference":[{"issue":"1","key":"6_CR1","first-page":"1","volume":"31","author":"K Ahrabian","year":"2018","unstructured":"Ahrabian, K., Babaali, B.: On usage of autoencoders and siamese networks for online handwritten signature verification. Neural Comput. 31(1), 1\u201314 (2018)","journal-title":"Neural Comput."},{"issue":"11","key":"6_CR2","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1007\/s00500-017-2782-5","volume":"23","author":"R Al-Hmouz","year":"2019","unstructured":"Al-Hmouz, R., Pedrycz, W., Daqrouq, K., Morfeq, A.: Quantifying dynamic time warping distance using probabilistic model in verification of dynamic signatures. Soft. Comput. 23(11), 407\u2013418 (2019). https:\/\/doi.org\/10.1007\/s00500-017-2782-5","journal-title":"Soft. Comput."},{"key":"6_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1007\/978-3-030-58526-6_12","volume-title":"Computer Vision \u2013 ECCV 2020","author":"A Dutta","year":"2020","unstructured":"Dutta, A., Verma, Y., Jawahar, C.V.: Recurrent image annotation with explicit inter-label dependencies. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12374, pp. 191\u2013207. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58526-6_12"},{"key":"6_CR4","unstructured":"Chandra Sekhar, V., Doctor, A., Viswanath, P.: A light weight and hybrid deep learning model based online signature verification. In: 2nd International Workshop on Machine Learning (ICDAR-WML), pp. 53\u201359 (2019)"},{"issue":"1","key":"6_CR5","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1109\/TCYB.2016.2630419","volume":"48","author":"M Diaz","year":"2018","unstructured":"Diaz, M., Fischer, A., Ferrer, M., Plamondon, R.: Dynamic signature verification system based on one real signature. IEEE Trans. Cybern. 48(1), 228\u2013239 (2018)","journal-title":"IEEE Trans. Cybern."},{"key":"6_CR6","unstructured":"Dikshant, G., Aditya, A., Nehal, M., Vineeth, N.S., Jawahar, C.V.: A multi-space approach to zero-shot object detection. In: Winter Conference on Applications of Computer Vision (WACV), pp. 1209\u20131217 (2020)"},{"issue":"1","key":"6_CR7","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1016\/j.ins.2018.05.049","volume":"460","author":"R Doroz","year":"2018","unstructured":"Doroz, R., Kudlacik, P., Porwika, P.: Online signature verification modeled by stability oriented reference signatures. Inf. Sci. 460(1), 151\u2013171 (2018)","journal-title":"Inf. Sci."},{"key":"6_CR8","unstructured":"Fisher, Y., Vladlen, K.: Multi-scale context aggregation by dilated convolutions. ICLR (2016)"},{"key":"6_CR9","first-page":"3113","volume":"150","author":"OJ Garcia","year":"2003","unstructured":"Garcia, O.J., Aguilar, J.F., Simon, D.: MCYT baseline corpus: a bimodal database. IEEE Proc. Vis. Image Sig. Process. 150, 3113\u20133123 (2003)","journal-title":"IEEE Proc. Vis. Image Sig. Process."},{"issue":"1","key":"6_CR10","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1007\/s11042-019-7264-6","volume":"78","author":"L He","year":"2019","unstructured":"He, L., Tan, H., Huang, Z.: Online handwritten signature verification based on association of curvature and torsion feature with Hausdorff distance. Multimed. Tools Appl. 78(1), 253\u2013278 (2019). https:\/\/doi.org\/10.1007\/s11042-019-7264-6","journal-title":"Multimed. Tools Appl."},{"issue":"1","key":"6_CR11","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1109\/TIM.2017.2755898","volume":"67","author":"B Kar","year":"2018","unstructured":"Kar, B., Mukherjee, A., Dutta, P.: Stroke point warping-based reference selection and verification of online signature. IEEE Trans. Instrum. Meas. 67(1), 2\u201311 (2018)","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"6","key":"6_CR12","doi-asserted-by":"publisher","first-page":"1624","DOI":"10.1109\/TIFS.2018.2883152","volume":"14","author":"S Lai","year":"2018","unstructured":"Lai, S., Jin, L.: Recurrent adaptation networks for online signature verification. IEEE Trans. Inf. Forensics Secur. 14(6), 1624\u20131637 (2018)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"6_CR13","doi-asserted-by":"crossref","unstructured":"Lai, S., Jin, L., Lin, L., Zhu, Y., Mao, H.: SynSig2vec: learning representations from synthetic dynamic signatures for real-world verification. In: AAAI Conference on Artificial Intelligence (2020)","DOI":"10.1609\/aaai.v34i01.5416"},{"key":"6_CR14","doi-asserted-by":"crossref","unstructured":"Li, C., Zhang, X., Lin, F.: A stroke-based RNN for writer-independent online signature verification. In: International Conference on Document Analysis and Recognition (ICDAR), pp. 526\u2013532 (2019)","DOI":"10.1109\/ICDAR.2019.00090"},{"key":"6_CR15","unstructured":"Li, W., Dong, L., Yousong, Z., Lu, T., Yi, S.: Dual super-resolution learning for semantic segmentation. In: 2020 Conference on Computer Vision and Pattern Recognition (CVPR), pp. 13774\u201313784 (2020)"},{"key":"6_CR16","first-page":"1","volume":"80","author":"X Liyang","year":"2022","unstructured":"Liyang, X., Zhongcheng, W., Xian, Z., Yong, L., Xinkuang, W.: Writer-independent online signature verification based on 2D representation of time series data using triplet supervised network. Measurement 80, 1\u201328 (2022)","journal-title":"Measurement"},{"issue":"12","key":"6_CR17","doi-asserted-by":"publisher","first-page":"2807","DOI":"10.1109\/TPAMI.2018.2869163","volume":"41","author":"A Moises","year":"2019","unstructured":"Moises, A., Miguel, F., Jose, J.: Anthropomorphic features for on-line signatures. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 41(12), 2807\u20132819 (2019)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell. (PAMI)"},{"issue":"1","key":"6_CR18","first-page":"117","volume":"117","author":"D Moises","year":"2019","unstructured":"Moises, D., Miguel, A.F., Donato, D.: A perspective analysis of handwritten signature technology. ACM Comput. Surv. 117(1), 117\u2013139 (2019)","journal-title":"ACM Comput. Surv."},{"issue":"1","key":"6_CR19","first-page":"1","volume":"102","author":"M Okawa","year":"2020","unstructured":"Okawa, M.: Online signature verification using single-template matching with time-series averaging and gradient boosting. Pattern Recogn. 102(1), 1\u201339 (2020)","journal-title":"Pattern Recogn."},{"issue":"1","key":"6_CR20","first-page":"1","volume":"112","author":"M Okawa","year":"2020","unstructured":"Okawa, M.: Time-series averaging and local stability-weighted dynamic time warping for online signature verification. Pattern Recogn. 112(1), 1\u201339 (2020)","journal-title":"Pattern Recogn."},{"key":"6_CR21","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1007\/978-3-319-08979-9_37","volume-title":"Machine Learning and Data Mining in Pattern Recognition","author":"S Otte","year":"2018","unstructured":"Otte, S., Liwicki, M., Krechel, D.: Investigating long short-term memory networks for various pattern recognition problems. In: Perner, P. (ed.) MLDM 2014. LNCS (LNAI), vol. 8556, pp. 484\u2013497. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-08979-9_37"},{"issue":"6","key":"6_CR22","doi-asserted-by":"publisher","first-page":"933","DOI":"10.1109\/TIFS.2014.2316472","volume":"9","author":"N Sae-Bae","year":"2014","unstructured":"Sae-Bae, N., Memon, N.: Online signature verification on mobile devices. IEEE Trans. Inf. Forensics Secur. 9(6), 933\u2013947 (2014)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"6_CR23","doi-asserted-by":"crossref","unstructured":"Sekhar, V., Gorthi, R.S., Viswanath, P.: Online signature verification by few-shot separable convolution based deep learning. In: 15th International Conference on Document Analysis and Recognition (ICDAR), pp. 1125\u20131129 (2019)","DOI":"10.1109\/ICDAR.2019.00182"},{"issue":"6","key":"6_CR24","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1049\/iet-bmt.2020.0032","volume":"9","author":"VC Sekhar","year":"2020","unstructured":"Sekhar, V.C., Viswanath, P., Prerana, M., Abhishek, S.: DeepFuseOSV: online signature verification using hybrid feature fusion and depthwise separable convolution neural network architecture. IET Biometrics 9(6), 259\u2013268 (2020)","journal-title":"IET Biometrics"},{"issue":"7","key":"6_CR25","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1016\/j.neucom.2020.05.072","volume":"409","author":"CS Vorugunti","year":"2020","unstructured":"Vorugunti, C.S., Pulabaigari, V., Gorthi, R.K.S.S., Mukherjee, P.: OSVFuseNet: online signature verification by feature fusion and depthwise separable convolution based deep learning. Neurocomputing 409(7), 157\u2013172 (2020)","journal-title":"Neurocomputing"},{"issue":"1","key":"6_CR26","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.patrec.2016.07.015","volume":"84","author":"A Sharma","year":"2016","unstructured":"Sharma, A., Sundaram, S.: An enhanced contextual DTW based system for online signature verification using vector quantization. Pattern Recogn. Lett. 84(1), 22\u201328 (2016)","journal-title":"Pattern Recogn. Lett."},{"issue":"3","key":"6_CR27","doi-asserted-by":"publisher","first-page":"705","DOI":"10.1109\/TIFS.2016.2632063","volume":"12","author":"A Sharma","year":"2017","unstructured":"Sharma, A., Sundaram, S.: A novel online signature verification system based on GMM features in a DTW framework. IEEE Trans. Inf. Forensics Secur. 12(3), 705\u2013718 (2017)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"issue":"2","key":"6_CR28","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1109\/TCYB.2017.2647826","volume":"48","author":"A Sharma","year":"2017","unstructured":"Sharma, A., Sundaram, S.: On the exploration of information from the DTW cost matrix for online signature verification. IEEE Trans. Cybern. 48(2), 611\u2013624 (2017)","journal-title":"IEEE Trans. Cybern."},{"key":"6_CR29","unstructured":"Sindhu, H., Prajwal, R., Rudrabha, M., Vinay, N., Jawahar, C.V.: Visual speech enhancement without a real visual stream. In: Workshop on Applications of Computer Vision (WACV), pp. 1\u201310 (2021)"},{"issue":"6","key":"6_CR30","doi-asserted-by":"publisher","first-page":"1624","DOI":"10.1109\/TIFS.2018.2883152","volume":"14","author":"L Songxuan","year":"2019","unstructured":"Songxuan, L., Jin, L.: Recurrent adaptation networks for online signature verification. IEEE Trans. Inf. Forensics Secur. 14(6), 1624\u20131637 (2019)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"issue":"5","key":"6_CR31","doi-asserted-by":"publisher","first-page":"11875","DOI":"10.1007\/s00521-019-04669-w","volume":"32","author":"C Subhash","year":"2020","unstructured":"Subhash, C.: Verification of dynamic signature using machine learning approach. Neural Comput. Appl. 32(5), 11875\u201311895 (2020). https:\/\/doi.org\/10.1007\/s00521-019-04669-w","journal-title":"Neural Comput. Appl."},{"issue":"19","key":"6_CR32","doi-asserted-by":"publisher","first-page":"12347","DOI":"10.1007\/s00521-021-05838-6","volume":"33","author":"S Chandra","year":"2021","unstructured":"Chandra, S., Singh, K.K., Kumar, S., Ganesh, K.V.K.S., Sravya, L., Kumar, B.P.: A novel approach to validate online signature using machine learning based on dynamic features. Neural Comput. Appl. 33(19), 12347\u201312366 (2021). https:\/\/doi.org\/10.1007\/s00521-021-05838-6","journal-title":"Neural Comput. Appl."},{"key":"6_CR33","doi-asserted-by":"crossref","unstructured":"Sun, W., Zhang, X., He, X.: Lightweight image classifier using dilated and depthwise separable convolutions. J. Cloud Comput. 9(55) (2020)","DOI":"10.1186\/s13677-020-00203-9"},{"key":"6_CR34","unstructured":"SVC: Svc-2004 task 1 and task 2 dataset. https:\/\/cse.hkust.edu.hk\/svc2004\/download.html (2004)"},{"key":"6_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11042-021-11063-z","volume":"80","author":"H Tan","year":"2021","unstructured":"Tan, H., He, L., Huang, Z.C., Zhan, H.: Online signature verification based on dynamic features from gene expression programming. Multimed. Tools Appl. 80, 1\u201327 (2021). https:\/\/doi.org\/10.1007\/s11042-021-11063-z","journal-title":"Multimed. Tools Appl."},{"issue":"4","key":"6_CR36","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1109\/TIFS.2017.2769023","volume":"13","author":"L Tang","year":"2018","unstructured":"Tang, L., Kang, W., Fang, Y.: Information divergence-based matching strategy for online signature verification. IEEE Trans. Inf. Forensics Secur. 13(4), 861\u2013873 (2018)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"6_CR37","doi-asserted-by":"crossref","unstructured":"Tolosana, R., Vera-Rodriguez, R., Fierrez, J., Ortega-Garcia, J.: Biometric signature verification using recurrent neural networks. In: 14th International Conference on Document Analysis and Recognition (ICDAR), pp. 652\u2013657 (2017)","DOI":"10.1109\/ICDAR.2017.112"},{"issue":"1","key":"6_CR38","first-page":"1","volume":"20","author":"R Tolosana","year":"2017","unstructured":"Tolosana, R., Vera-Rodriguez, R., Fierrez, J., Ortega-Garcia, J.: DeepSign: deep on-line signature verification. Arxiv 20(1), 1\u201310 (2017)","journal-title":"Arxiv"},{"key":"6_CR39","doi-asserted-by":"publisher","first-page":"5128","DOI":"10.1109\/ACCESS.2018.2793966","volume":"6","author":"R Tolosana","year":"2018","unstructured":"Tolosana, R., Vera-Rodriguez, R., Fierrez, J., Ortega-Garcia, J.: Exploring recurrent neural networks for on-line handwritten signature biometrics. IEEE Access 6, 5128\u20135138 (2018)","journal-title":"IEEE Access"},{"key":"6_CR40","unstructured":"Xiaomeng, W., Akisato, K., Brian, I.K., Seiichi, U., Kunio, K.: Deep dynamic time warping: end-to-end local representation learning for online signature verification. In: 14th International Conference on Document Analysis and Recognition (ICDAR), pp. 1103\u20131110 (2019)"},{"issue":"3","key":"6_CR41","doi-asserted-by":"publisher","first-page":"7811","DOI":"10.1007\/s00500-018-3477-2","volume":"22","author":"L Yang","year":"2018","unstructured":"Yang, L., Cheng, Y., Wang, X., Liu, Q.: Online handwritten signature verification using feature weighting algorithm relief. Soft. Comput. 22(3), 7811\u20137823 (2018). https:\/\/doi.org\/10.1007\/s00500-018-3477-2","journal-title":"Soft. Comput."},{"key":"6_CR42","doi-asserted-by":"crossref","unstructured":"Zhengyang, W., Shuiwang, J.: Smoothed dilated convolutions for improved dense prediction. In: 24th International Conference on Knowledge Discovery and Data Mining, pp. 2486\u20132495 (2018)","DOI":"10.1145\/3219819.3219944"}],"container-title":["Lecture Notes in Computer Science","Frontiers in Handwriting Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-21648-0_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T14:31:58Z","timestamp":1728484318000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-21648-0_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031216473","9783031216480"],"references-count":42,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-21648-0_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"25 November 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICFHR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Frontiers in Handwriting Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hyderabad","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":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 December 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icfhr2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/icfhr2022.org","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":"Easychair","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":"36","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":"1","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":"59% - 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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}