{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,25]],"date-time":"2025-07-25T10:16:06Z","timestamp":1753438566159,"version":"3.40.3"},"publisher-location":"Cham","reference-count":48,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031314162"},{"type":"electronic","value":"9783031314179"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-31417-9_34","type":"book-chapter","created":{"date-parts":[[2023,5,6]],"date-time":"2023-05-06T12:02:31Z","timestamp":1683374551000},"page":"443-457","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["FAV-Net: A Simple Single-Shot Self-attention Based ForeArm-Vein Biometric"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4640-5164","authenticated-orcid":false,"given":"Shitala","family":"Prasad","sequence":"first","affiliation":[]},{"given":"Chaoying","family":"Tang","sequence":"additional","affiliation":[]},{"given":"Yufeng","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Biao","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,5,7]]},"reference":[{"key":"34_CR1","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/j.patrec.2020.05.030","volume":"136","author":"Y Aberni","year":"2020","unstructured":"Aberni, Y., Boubchir, L., Daachi, B.: Palm vein recognition based on competitive coding scheme using multi-scale local binary pattern with ant colony optimization. PRL 136, 101\u2013110 (2020)","journal-title":"PRL"},{"key":"34_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1007\/978-3-319-78759-6_23","volume-title":"Bioinformatics and Biomedical Engineering","author":"Orcan Alpar","year":"2018","unstructured":"Alpar, Orcan, Krejcar, Ondrej: Thermal imaging for localization of anterior forearm subcutaneous veins. In: Rojas, Ignacio, Ortu\u00f1o, Francisco (eds.) IWBBIO 2018. LNCS, vol. 10814, pp. 243\u2013254. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-78759-6_23"},{"key":"34_CR3","doi-asserted-by":"publisher","unstructured":"Chai, T., Li, J., Prasad, S., Lu, Q., Zhang, Z.: Shape-driven lightweight CNN for finger-vein biometrics. J. Inf. Secur. Appl. 67, 103211 (2022). https:\/\/doi.org\/10.1016\/j.jisa.2022.103211. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2214212622000886","DOI":"10.1016\/j.jisa.2022.103211"},{"key":"34_CR4","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.future.2019.04.013","volume":"99","author":"T Chai","year":"2019","unstructured":"Chai, T., Prasad, S., Wang, S.: Boosting palmprint identification with gender information using deepnet. FGCS 99, 41\u201353 (2019)","journal-title":"FGCS"},{"issue":"9","key":"34_CR5","first-page":"4594","volume":"28","author":"Z Chen","year":"2019","unstructured":"Chen, Z., Fu, Y., Zhang, Y., Jiang, Y.G., Xue, X., Sigal, L.: Multi-level semantic feature augmentation for one-shot learning. IEEE TIP 28(9), 4594\u20134605 (2019)","journal-title":"IEEE TIP"},{"key":"34_CR6","doi-asserted-by":"crossref","unstructured":"Choras, R.S.: Personal identification using forearm vein patterns. In: IWOB, pp. 1\u20135. IEEE (2017)","DOI":"10.1109\/IWOBI.2017.7985519"},{"key":"34_CR7","doi-asserted-by":"crossref","unstructured":"Deng, J., Guo, J., Xue, N., Zafeiriou, S.: ArcFace: additive angular margin loss for deep face recognition. In: CVPR, pp. 4690\u20134699 (2019)","DOI":"10.1109\/CVPR.2019.00482"},{"key":"34_CR8","doi-asserted-by":"publisher","first-page":"104801","DOI":"10.1109\/ACCESS.2020.3000044","volume":"8","author":"R Garcia-Martin","year":"2020","unstructured":"Garcia-Martin, R., Sanchez-Reillo, R.: Vein biometric recognition on a smartphone. IEEE Access 8, 104801\u2013104813 (2020)","journal-title":"IEEE Access"},{"key":"34_CR9","unstructured":"Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: ICAIS, pp. 249\u2013256. JMLR Workshop and Conference Proceedings (2010)"},{"issue":"18","key":"34_CR10","doi-asserted-by":"publisher","first-page":"13225","DOI":"10.1016\/j.eswa.2012.05.079","volume":"39","author":"WY Han","year":"2012","unstructured":"Han, W.Y., Lee, J.C.: Palm vein recognition using adaptive Gabor filter. Expert Syst. Appl. 39(18), 13225\u201313234 (2012)","journal-title":"Expert Syst. Appl."},{"key":"34_CR11","doi-asserted-by":"crossref","unstructured":"Hassan, B., Izquierdo, E., Piatrik, T.: Soft biometrics: a survey. In: MTAP, pp. 1\u201344 (2021)","DOI":"10.1007\/s11042-021-10622-8"},{"key":"34_CR12","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"34_CR13","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: CVPR, pp. 4700\u20134708 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"34_CR14","doi-asserted-by":"publisher","unstructured":"Jain, A.K., Flynn, P., Ross, A.A.: Handbook of biometrics. Springer Science & Business Media (2007). https:\/\/doi.org\/10.1007\/978-0-387-71041-9","DOI":"10.1007\/978-0-387-71041-9"},{"key":"34_CR15","first-page":"2641","volume":"15","author":"RS Kuzu","year":"2020","unstructured":"Kuzu, R.S., Piciucco, E., Maiorana, E., Campisi, P.: On-the-fly finger-vein-based biometric recognition using deep neural networks. IEEE TIFS 15, 2641\u20132654 (2020)","journal-title":"IEEE TIFS"},{"key":"34_CR16","unstructured":"Kuzu, R.S., Maiorana, E., Campisi, P.: Loss functions for CNN-based biometric vein recognition. In: EUSIPCO, pp. 750\u2013754. IEEE (2020)"},{"key":"34_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"1290","DOI":"10.1007\/978-3-642-01793-3_130","volume-title":"Advances in Biometrics","author":"Pierre-Olivier Ladoux","year":"2009","unstructured":"Ladoux, Pierre-Olivier., Rosenberger, Christophe, Dorizzi, Bernadette: Palm vein verification system based on SIFT matching. In: Tistarelli, Massimo, Nixon, Mark S.. (eds.) ICB 2009. LNCS, vol. 5558, pp. 1290\u20131298. Springer, Heidelberg (2009). https:\/\/doi.org\/10.1007\/978-3-642-01793-3_130"},{"key":"34_CR18","doi-asserted-by":"crossref","unstructured":"Lane, L.: NIST finds flaws in facial checks on people with COVID masks. Biometric Technology Today (2020)","DOI":"10.1016\/S0969-4765(20)30101-6"},{"issue":"3","key":"34_CR19","doi-asserted-by":"publisher","first-page":"647","DOI":"10.22581\/muet1982.2003.19","volume":"39","author":"M Leghari","year":"2020","unstructured":"Leghari, M., Memon, S., Dhomeja, L.D., Jalbani, A.H., et al.: Analyzing the effects of data augmentation on single and multimodal biometrics. Mehran Univ. Res. J. Eng. Technol. 39(3), 647 (2020)","journal-title":"Mehran Univ. Res. J. Eng. Technol."},{"issue":"5","key":"34_CR20","doi-asserted-by":"publisher","first-page":"3012","DOI":"10.1007\/s10489-020-02100-9","volume":"51","author":"Y Li","year":"2021","unstructured":"Li, Y., Guo, K., Lu, Y., Liu, L.: Cropping and attention based approach for masked face recognition. Appl. Intell. 51(5), 3012\u20133025 (2021). https:\/\/doi.org\/10.1007\/s10489-020-02100-9","journal-title":"Appl. Intell."},{"key":"34_CR21","doi-asserted-by":"crossref","unstructured":"Liu, W., Li, W., Sun, L., Zhang, L., Chen, P.: Finger vein recognition based on deep learning. In: ICIEA, pp. 205\u2013210. IEEE (2017)","DOI":"10.1109\/ICIEA.2017.8282842"},{"key":"34_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1007\/978-3-030-01264-9_8","volume-title":"Computer Vision \u2013 ECCV 2018","author":"Ningning Ma","year":"2018","unstructured":"Ma, Ningning, Zhang, Xiangyu, Zheng, Hai-Tao., Sun, Jian: ShuffleNet V2: practical guidelines for efficient CNN architecture design. In: Ferrari, Vittorio, Hebert, Martial, Sminchisescu, Cristian, Weiss, Yair (eds.) Computer Vision \u2013 ECCV 2018. LNCS, vol. 11218, pp. 122\u2013138. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01264-9_8"},{"key":"34_CR23","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"753","DOI":"10.1007\/978-981-15-8289-9_72","volume-title":"ICT Systems and Sustainability","author":"Sakorn Mekruksavanich","year":"2021","unstructured":"Mekruksavanich, Sakorn, Jitpattanakul, Anuchit: Convolutional neural network and data augmentation for behavioral-based biometric user identification. In: Tuba, Milan, Akashe, Shyam, Joshi, Amit (eds.) ICT Systems and Sustainability. AISC, vol. 1270, pp. 753\u2013761. Springer, Singapore (2021). https:\/\/doi.org\/10.1007\/978-981-15-8289-9_72"},{"key":"34_CR24","doi-asserted-by":"crossref","unstructured":"Nikisins, O., Eglitis, T., Anjos, A., Marcel, S.: Fast cross-correlation based wrist vein recognition algorithm with rotation and translation compensation. In: IWB, pp. 1\u20137. IEEE (2018)","DOI":"10.1109\/IWBF.2018.8401550"},{"key":"34_CR25","doi-asserted-by":"crossref","unstructured":"Pascual, J.E.S., Uriarte-Antonio, J., Sanchez-Reillo, R., Lorenz, M.G.: Capturing hand or wrist vein images for biometric authentication using low-cost devices. In: CIIHMSP, pp. 318\u2013322. IEEE (2010)","DOI":"10.1109\/IIHMSP.2010.85"},{"key":"34_CR26","doi-asserted-by":"crossref","unstructured":"Peng, C., Chen, M., Jiang, X.: Under-display ultrasonic fingerprint recognition with finger vessel imaging. IEEE Sensors J. 21, 7412\u20137419 (2021)","DOI":"10.1109\/JSEN.2021.3051975"},{"issue":"2","key":"34_CR27","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1093\/comjnl\/bxaa045","volume":"65","author":"S Prasad","year":"2022","unstructured":"Prasad, S., Chai, T.: Palmprint for individual\u2019s personality behavior analysis. Comput. J. 65(2), 355\u2013370 (2022)","journal-title":"Comput. J."},{"key":"34_CR28","doi-asserted-by":"crossref","unstructured":"Prasad, S., Li, Y., Lin, D., Sheng, D.: maskedFaceNet: a progressive semi-supervised masked face detector. In: WACV, pp. 3389\u20133398 (2021)","DOI":"10.1109\/WACV48630.2021.00343"},{"key":"34_CR29","doi-asserted-by":"crossref","unstructured":"Ratha, N.K., Bolle, R.M.: Effect of controlled image acquisition on fingerprint matching. In: ICPR, vol. 2, pp. 1659\u20131661. IEEE (1998)","DOI":"10.1109\/ICPR.1998.712037"},{"key":"34_CR30","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/j.patrec.2020.12.021","volume":"143","author":"U Saeed","year":"2021","unstructured":"Saeed, U.: Facial micro-expressions as a soft biometric for person recognition. PRL 143, 95\u2013103 (2021)","journal-title":"PRL"},{"key":"34_CR31","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: MobileNetV2: Inverted residuals and linear bottlenecks. In: CVPR, pp. 4510\u20134520 (2018)","DOI":"10.1109\/CVPR.2018.00474"},{"issue":"9","key":"34_CR32","doi-asserted-by":"publisher","first-page":"213","DOI":"10.3390\/info9090213","volume":"9","author":"K Shaheed","year":"2018","unstructured":"Shaheed, K., Liu, H., Yang, G., Qureshi, I., Gou, J., Yin, Y.: A systematic review of finger vein recognition techniques. Information 9(9), 213 (2018)","journal-title":"Information"},{"issue":"6","key":"34_CR33","first-page":"402","volume":"150","author":"D Simon-Zorita","year":"2003","unstructured":"Simon-Zorita, D., Ortega-Garcia, J., Fierrez-Aguilar, J., Gonzalez-Rodriguez, J.: Image quality and position variability assessment in minutiae-based fingerprint verification. VISP 150(6), 402\u2013408 (2003)","journal-title":"VISP"},{"key":"34_CR34","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"34_CR35","doi-asserted-by":"crossref","unstructured":"Solano, J., Tengana, L., Castelblanco, A., Rivera, E., Lopez, C., Ochoa, M.: A few-shot practical behavioral biometrics model for login authentication in web applications. In: NDSS Workshop on MADWeb (2020)","DOI":"10.14722\/madweb.2020.23011"},{"key":"34_CR36","doi-asserted-by":"crossref","unstructured":"Stewart, R.F., Estevao, M., Adler, A.: Fingerprint recognition performance in rugged outdoors and cold weather conditions. In: ICB: Theory, Applications, and Systems, pp. 1\u20136. IEEE (2009)","DOI":"10.1109\/BTAS.2009.5339061"},{"key":"34_CR37","doi-asserted-by":"crossref","unstructured":"Sun, B., Tao, X., Luo, X., et al.: Research on palm vein recognition algorithm based on improved convolutional neural network. In: CACS, pp. 1\u20136. IEEE (2020)","DOI":"10.1109\/CACS50047.2020.9289736"},{"key":"34_CR38","doi-asserted-by":"crossref","unstructured":"Tan, M., et al.: MnasNet: platform-aware neural architecture search for mobile. In: CVPR, pp. 2820\u20132828 (2019)","DOI":"10.1109\/CVPR.2019.00293"},{"key":"34_CR39","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.inffus.2015.08.004","volume":"32","author":"C Tang","year":"2016","unstructured":"Tang, C., Zhang, H., Kong, A.W.K.: Using multiple models to uncover blood vessel patterns in color images for forensic analysis. Inf. Fusion 32, 26\u201339 (2016)","journal-title":"Inf. Fusion"},{"key":"34_CR40","first-page":"130","volume":"136","author":"H Wang","year":"2017","unstructured":"Wang, H., Gu, J., Wang, S.: An effective intrusion detection framework based on SVM with feature augmentation. KBS 136, 130\u2013139 (2017)","journal-title":"KBS"},{"issue":"11","key":"34_CR41","first-page":"2599","volume":"12","author":"J Wang","year":"2017","unstructured":"Wang, J., Wang, G.: Quality-specific hand vein recognition system. IEEE TIFS 12(11), 2599\u20132610 (2017)","journal-title":"IEEE TIFS"},{"issue":"11","key":"34_CR42","doi-asserted-by":"publisher","first-page":"2870","DOI":"10.1016\/j.jss.2013.06.065","volume":"86","author":"KS Wu","year":"2013","unstructured":"Wu, K.S., Lee, J.C., Lo, T.M., Chang, K.C., Chang, C.P.: A secure palm vein recognition system. J. Syst. Softw. 86(11), 2870\u20132876 (2013)","journal-title":"J. Syst. Softw."},{"issue":"7","key":"34_CR43","first-page":"4244","volume":"15","author":"W Yang","year":"2019","unstructured":"Yang, W., Wang, S., Hu, J., Zheng, G., Yang, J., Valli, C.: Securing deep learning based edge finger vein biometrics with binary decision diagram. IEEE TII 15(7), 4244\u20134253 (2019)","journal-title":"IEEE TII"},{"key":"34_CR44","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1007\/978-3-642-25449-9_33","volume-title":"Biometric Recognition","author":"Yilong Yin","year":"2011","unstructured":"Yin, Yilong, Liu, Lili, Sun, Xiwei: SDUMLA-HMT: a multimodal biometric database. In: Sun, Zhenan, Lai, Jianhuang, Chen, Xilin, Tan, Tieniu (eds.) CCBR 2011. LNCS, vol. 7098, pp. 260\u2013268. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-25449-9_33"},{"key":"34_CR45","doi-asserted-by":"crossref","unstructured":"Zanlorensi, L.A., Proen\u00e7a, H., Menotti, D.: Unconstrained periocular recognition: Using generative deep learning frameworks for attribute normalization. In: ICIP, pp. 1361\u20131365. IEEE (2020)","DOI":"10.1109\/ICIP40778.2020.9191251"},{"key":"34_CR46","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Qin, J., Lv, L., Wang, Z.: Based on Siamese network with self-attention model for gait recognition. In: ICMA, pp. 1118\u20131122. IEEE (2020)","DOI":"10.1109\/ICMA49215.2020.9233689"},{"key":"34_CR47","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1007\/978-3-030-03398-9_4","volume-title":"Pattern Recognition and Computer Vision","author":"Dexing Zhong","year":"2018","unstructured":"Zhong, Dexing, Liu, Shuming, Wang, Wenting, Du, Xuefeng: Palm vein recognition with deep hashing network. In: Lai, J.-H., et al. (eds.) PRCV 2018. LNCS, vol. 11256, pp. 38\u201349. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-03398-9_4"},{"key":"34_CR48","doi-asserted-by":"crossref","unstructured":"Zhong, Y., Deng, W., Hu, J., Zhao, D., Li, X., Wen, D.: SFace: sigmoid-constrained hypersphere loss for robust face recognition. In: IEEE TIP (2021)","DOI":"10.1109\/TIP.2020.3048632"}],"container-title":["Communications in Computer and Information Science","Computer Vision and Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-31417-9_34","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,19]],"date-time":"2024-10-19T22:46:33Z","timestamp":1729377993000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-31417-9_34"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031314162","9783031314179"],"references-count":48,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-31417-9_34","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"7 May 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CVIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computer Vision and Image Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Nagpur","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 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 November 2022","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":"cvip2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/vnit.ac.in\/cvip2022\/","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":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"307","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":"110","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":"11","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","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)"}}]}}