{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T16:29:42Z","timestamp":1778603382148,"version":"3.51.4"},"reference-count":69,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2022,11,22]],"date-time":"2022-11-22T00:00:00Z","timestamp":1669075200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,11,22]],"date-time":"2022-11-22T00:00:00Z","timestamp":1669075200000},"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":["Neural Comput & Applic"],"published-print":{"date-parts":[[2023,3]]},"DOI":"10.1007\/s00521-022-07894-y","type":"journal-article","created":{"date-parts":[[2022,11,23]],"date-time":"2022-11-23T10:18:39Z","timestamp":1669198719000},"page":"6471-6497","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":54,"title":["Improving automated latent fingerprint detection and segmentation using deep convolutional neural network"],"prefix":"10.1007","volume":"35","author":[{"given":"Megha","family":"Chhabra","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kiran Kumar","family":"Ravulakollu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Manoj","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abhay","family":"Sharma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9821-6146","authenticated-orcid":false,"given":"Anand","family":"Nayyar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,11,22]]},"reference":[{"issue":"15","key":"7894_CR1","doi-asserted-by":"crossref","first-page":"23605","DOI":"10.1007\/s11042-020-10207-x","volume":"80","author":"D Agarwal","year":"2021","unstructured":"Agarwal D, Bansal A (2021) A utility of pores as level 3 features in latent fingerprint identification. Multimed Tools Appl 80(15):23605\u201323624","journal-title":"Multimed Tools Appl"},{"issue":"101668","key":"7894_CR2","volume":"56","author":"H Ali","year":"2020","unstructured":"Ali H, Sharif M, Yasmin M, Rehmani MH (2020) Colour-based template selection for detection of gastric abnormalities in video endoscopy. Biomed Signal Process Control 56(101668):101668","journal-title":"Biomed Signal Process Control"},{"issue":"2","key":"7894_CR3","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1007\/s10916-019-1483-2","volume":"44","author":"J Amin","year":"2019","unstructured":"Amin J, Sharif M, Gul N, Raza M, Anjum MA, Nisar MW, Bukhari SAC (2019) Brain tumor detection by using stacked autoencoders in deep learning. J Med Syst 44(2):32","journal-title":"J Med Syst"},{"issue":"4","key":"7894_CR4","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1007\/s10529-020-03052-3","volume":"43","author":"AR Azman","year":"2021","unstructured":"Azman AR, Mahat NA, Wahab RA, Ahmad WA, Puspanadan JK, Huri MAM, Kamaluddin MR, Ismail D (2021) Box-behnken design optimisation of a green novel nanobio-based reagent for rapid visualisation of latent fingerprints on wet. Non-porous substrates. Biotechnol Lett 43(4):881\u2013898","journal-title":"Biotechnol Lett"},{"issue":"101952","key":"7894_CR5","doi-asserted-by":"crossref","first-page":"101952","DOI":"10.1016\/j.media.2020.101952","volume":"69","author":"C Baur","year":"2021","unstructured":"Baur C, Denner S, Wiestler B, Navab N, Albarqouni S (2021) Autoencoders for unsupervised anomaly segmentation in brain mr images: a comparative study. Med Image Anal 69(101952):101952","journal-title":"Med Image Anal"},{"issue":"2","key":"7894_CR6","doi-asserted-by":"crossref","first-page":"742","DOI":"10.1109\/TIP.2014.2383320","volume":"24","author":"A Borji","year":"2015","unstructured":"Borji A (2015) What is a salient object? A dataset and a baseline model for salient object detection. IEEE Trans Image Process 24(2):742\u2013756","journal-title":"IEEE Trans Image Process"},{"key":"7894_CR7","doi-asserted-by":"crossref","unstructured":"Cao K, Jain AK (2015) Latent orientation field estimation via convolutional neural network. In: 2015 International conference on biometrics (ICB). IEEE","DOI":"10.1109\/ICB.2015.7139060"},{"key":"7894_CR8","doi-asserted-by":"crossref","unstructured":"Chandraprabha K, Akila S (2019) Texture feature extraction for batik images using glcm and glrlm with neural network classification. Int J Sci Res Comput Sci Eng Inf Technol 06\u201315","DOI":"10.32628\/CSEIT195322"},{"issue":"4","key":"7894_CR9","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"L-C Chen","year":"2018","unstructured":"Chen L-C, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2018) DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans Pattern Anal Mach Intell 40(4):834\u2013848","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"3","key":"7894_CR10","doi-asserted-by":"crossref","first-page":"359","DOI":"10.3233\/IDT-190105","volume":"14","author":"M Chhabra","year":"2020","unstructured":"Chhabra M, Shukla MK, Ravulakollu KK (2020) Boosting the classification performance of latent fingerprint segmentation using cascade of classifiers. Intell Decis Technol 14(3):359\u2013371","journal-title":"Intell Decis Technol"},{"key":"7894_CR11","first-page":"189","volume-title":"Advances in intelligent systems and computing","author":"M Chhabra","year":"2021","unstructured":"Chhabra M, Shukla MK, Ravulakollu KK (2021) Bagging- and boosting-based latent fingerprint image classification and segmentation. Advances in intelligent systems and computing. Springer, Singapore, pp 189\u2013201"},{"issue":"6","key":"7894_CR12","doi-asserted-by":"crossref","first-page":"1115","DOI":"10.2174\/2213275912666190429153952","volume":"13","author":"M Chhabra","year":"2021","unstructured":"Chhabra M, Shukla MK, Ravulakollu KK (2021) State-of-the-art: a systematic literature review of image segmentation in latent fingerprint foren. Recent Pat Comput Sci 13(6):1115\u20131125","journal-title":"Recent Pat Comput Sci"},{"issue":"2","key":"7894_CR13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3177745","volume":"14","author":"M Cornia","year":"2018","unstructured":"Cornia M, Baraldi L, Serra G, Cucchiara R (2018) Paying more attention to saliency: image captioning with saliency and context attention. ACM Trans Multimed Comput Commun Appl 14(2):1\u201321","journal-title":"ACM Trans Multimed Comput Commun Appl"},{"key":"7894_CR14","doi-asserted-by":"crossref","DOI":"10.4324\/9781003037941","volume-title":"Courtroom testimony for fingerprint examiners","author":"HM Daluz","year":"2021","unstructured":"Daluz HM (2021) Courtroom testimony for fingerprint examiners. CRC Press, Florida"},{"issue":"6","key":"7894_CR15","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1007\/s11623-013-0141-y","volume":"37","author":"B Deufel","year":"2013","unstructured":"Deufel B, Mueller C, Duffy G, Kevenaar T (2013) BioPACE: biometric passwords for next generation authentication protocols for machine-readable travel documents. Datenschutz Datensicherheit - DuD 37(6):363\u2013366","journal-title":"Datenschutz Datensicherheit - DuD"},{"issue":"1","key":"7894_CR16","doi-asserted-by":"crossref","first-page":"012005","DOI":"10.1088\/1742-6596\/1844\/1\/012005","volume":"1844","author":"GSM Diyasa","year":"2021","unstructured":"Diyasa GSM, Fauzi A, Idhom M, Setiawan A (2021) Multi-face recognition for the detection of prisoners in jail using a modified cascade classifier and CNN. J Phys Conf Ser 1844(1):012005","journal-title":"J Phys Conf Ser"},{"key":"7894_CR17","doi-asserted-by":"crossref","unstructured":"Ezeobiejesi J, Bhanu B (2016) Latent fingerprint image segmentation using fractal dimension features and weighted extreme learning machine ensemble. In: 2016 IEEE conference on computer vision and pattern recognition workshops (CVPRW). IEEE","DOI":"10.1109\/CVPRW.2016.33"},{"key":"7894_CR18","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1007\/978-3-319-61657-5_4","volume-title":"Deep learning for biometrics","author":"J Ezeobiejesi","year":"2017","unstructured":"Ezeobiejesi J, Bhanu B (2017) Latent fingerprint image segmentation using deep neural network. Deep learning for biometrics. Springer International Publishing, Cham, pp 83\u2013107"},{"issue":"9","key":"7894_CR19","doi-asserted-by":"crossref","first-page":"1374","DOI":"10.3390\/electronics9091374","volume":"9","author":"R Fan","year":"2020","unstructured":"Fan R, Li X, Lee S, Li T, Zhang HL (2020) Smart image enhancement using CLAHE based on an F-shift transformation during decompression. Electronics 9(9):1374","journal-title":"Electronics"},{"issue":"6","key":"7894_CR20","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","volume":"3","author":"R Haralick","year":"1973","unstructured":"Haralick R, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybernet SMC 3(6):610\u2013621","journal-title":"IEEE Trans Syst Man Cybernet SMC"},{"key":"7894_CR21","volume-title":"Classification and uses of fingerprints","author":"ER Henry","year":"1900","unstructured":"Henry ER (1900) Classification and uses of fingerprints. Routledge & Sons, London"},{"key":"7894_CR22","doi-asserted-by":"publisher","first-page":"475","DOI":"10.3390\/e24040475","volume":"24","author":"C-T Hsiao","year":"2022","unstructured":"Hsiao C-T, Lin C-Y, Wang P-S, Wu Y-T (2022) Application of convolutional neural network for fingerprint-based prediction of gender, finger position, and height. Entropy (Basel) 24:475. https:\/\/doi.org\/10.3390\/e24040475","journal-title":"Entropy (Basel)"},{"issue":"1","key":"7894_CR23","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1007\/s10346-019-01274-9","volume":"17","author":"F Huang","year":"2020","unstructured":"Huang F, Zhang J, Zhou C, Wang Y, Huang J, Zhu L (2020) A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction. Landslides 17(1):217\u2013229","journal-title":"Landslides"},{"key":"7894_CR24","first-page":"125","volume":"272","author":"DP Igoe","year":"2018","unstructured":"Igoe DP, Parisi AV, Downs NJ, Amar A, Turner J (2018) Comparative signal to noise ratio as a determinant to select smartphone image sensor colour channels for analysis in the UVB. Sensors and actuators. A, Physical 272:125\u2013133","journal-title":"A, Physical"},{"key":"7894_CR25","doi-asserted-by":"publisher","DOI":"10.1007\/s11045-021-00789-6","author":"S Jalali","year":"2021","unstructured":"Jalali S, Boostani R, Mohammadi M (2021) Efficient fingerprint features for gender recognition. Multidimens Syst Signal Process. https:\/\/doi.org\/10.1007\/s11045-021-00789-6","journal-title":"Multidimens Syst Signal Process"},{"issue":"4","key":"7894_CR26","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1080\/20961790.2018.1521327","volume":"4","author":"BT Johnson","year":"2019","unstructured":"Johnson BT, Riemen JAJM (2019) Digital capture of fingerprints in a disaster victim identification setting: a review and case study. Forensic Sci Res 4(4):293\u2013302","journal-title":"Forensic Sci Res"},{"issue":"4","key":"7894_CR27","first-page":"443","volume":"70","author":"ND Kalka","year":"2020","unstructured":"Kalka ND, Beachler M, Hicklin RA (2020) LQMetric: a latent fingerprint quality metric for predicting AFIS performance and assessing the value of latent fingerprints. JFI 70(4):443\u2013463","journal-title":"JFI"},{"issue":"1","key":"7894_CR28","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1080\/08839514.2018.1526704","volume":"33","author":"AI Khan","year":"2019","unstructured":"Khan AI, Wani MA (2019) Patch-based segmentation of latent fingerprint images using convolutional neural network. Appl Artif Intell AAI 33(1):87\u2013100","journal-title":"Appl Artif Intell AAI"},{"issue":"103062","key":"7894_CR29","doi-asserted-by":"crossref","first-page":"103062","DOI":"10.1016\/j.jvcir.2021.103062","volume":"76","author":"R Kharghanian","year":"2021","unstructured":"Kharghanian R, Peiravi A, Moradi F, Iosifidis A (2021) Pain detection using batch normalized discriminant restricted boltzmann machine layers. J Vis Commun Image Represent 76(103062):103062","journal-title":"J Vis Commun Image Represent"},{"issue":"6","key":"7894_CR30","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2017) ImageNet classification with deep convolutional neural networks. Commun ACM 60(6):84\u201390","journal-title":"Commun ACM"},{"issue":"3","key":"7894_CR31","doi-asserted-by":"crossref","first-page":"1987","DOI":"10.3233\/JIFS-169910","volume":"36","author":"AS Kumar","year":"2019","unstructured":"Kumar AS, Nair JJ (2019) Pair wise training for stacked convolutional autoencoders using small scale images. J Intell Fuzzy Syst 36(3):1987\u20131995","journal-title":"J Intell Fuzzy Syst"},{"issue":"11","key":"7894_CR32","doi-asserted-by":"crossref","first-page":"5012","DOI":"10.1109\/TIP.2016.2602079","volume":"25","author":"G Li","year":"2016","unstructured":"Li G, Yu Y (2016) Visual saliency detection based on multiscale deep CNN features. IEEE Trans Image Process Pub IEEE Signal Process Soc 25(11):5012\u20135024","journal-title":"IEEE Trans Image Process Pub IEEE Signal Process Soc"},{"key":"7894_CR33","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.neucom.2019.09.073","volume":"374","author":"Y Liu","year":"2020","unstructured":"Liu Y, Zhang Y, Coleman S, Bhanu B, Liu S (2020) A new patch selection method based on parsing and saliency detection for person reidentification. Neurocomputing 374:86\u201399","journal-title":"Neurocomputing"},{"key":"7894_CR34","first-page":"188","volume":"352","author":"Yisi Liu","year":"2019","unstructured":"Liu Yisi, Wang X, Wang L, Liu D (2019) A modified leaky ReLU scheme (MLRS) for topology optimization with multiple materials. Appl Math Comput 352:188\u2013204","journal-title":"Appl Math Comput"},{"key":"7894_CR35","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-84882-254-2","volume-title":"Handbook of fingerprint recognition","author":"D Maltoni","year":"2009","unstructured":"Maltoni D, Maio D, Jain AK, Prabhakar S (2009) Handbook of fingerprint recognition. Springer-Verlag, Berlin"},{"key":"7894_CR36","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.isprsjprs.2017.11.009","volume":"135","author":"D Marmanis","year":"2018","unstructured":"Marmanis D, Schindler K, Wegner JD, Galliani S, Datcu M, Stilla U (2018) Classification with an edge: improving semantic image segmentation with boundary detection. ISPRS J Photogramm Remote Sens Off Pub Int Soc Photogramm Remote Sens (ISPRS) 135:158\u2013172","journal-title":"ISPRS J Photogramm Remote Sens Off Pub Int Soc Photogramm Remote Sens (ISPRS)"},{"key":"7894_CR37","first-page":"52","volume-title":"Lecture notes in computer science","author":"J Masci","year":"2011","unstructured":"Masci J, Meier U, Cire\u015fan D, Schmidhuber J (2011) Stacked convolutional auto-encoders for hierarchical feature extraction. Lecture notes in computer science. Springer, Berlin Heidelberg, pp 52\u201359"},{"issue":"1","key":"7894_CR38","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1109\/MMUL.2015.59","volume":"23","author":"VA Mateescu","year":"2016","unstructured":"Mateescu VA, Bajic IV (2016) Visual attention retargeting. IEEE Multimed 23(1):82\u201391","journal-title":"IEEE Multimed"},{"issue":"4","key":"7894_CR39","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1016\/0031-3203(87)90069-0","volume":"20","author":"BM Mehtre","year":"1987","unstructured":"Mehtre BM, Murthy NN, Kapoor S, Chatterjee B (1987) Segmentation of fingerprint images using the directional image. Pattern Recogn 20(4):429\u2013435","journal-title":"Pattern Recogn"},{"issue":"3","key":"7894_CR40","doi-asserted-by":"crossref","first-page":"901","DOI":"10.1007\/s40815-020-00824-x","volume":"22","author":"D M\u00fajica-Vargas","year":"2020","unstructured":"M\u00fajica-Vargas D, Kinani JMV, de Rubio J, J. (2020) Color-based image segmentation by means of a robust intuitionistic fuzzy C-means algorithm. Int J Fuzzy Syst 22(3):901\u2013916","journal-title":"Int J Fuzzy Syst"},{"key":"7894_CR41","unstructured":"Murshed MGS, Kline R, Bahmani K, Hussain F, Schuckers S (2021) Deep slap fingerprint segmentation for juveniles and adults. In arXiv [cs.CV]. http:\/\/arxiv.org\/abs\/2110.04067"},{"key":"7894_CR42","doi-asserted-by":"crossref","unstructured":"Nguyen D-L, Cao K, Jain AK (2018) Automatic latent fingerprint segmentation. In: 2018 IEEE 9th international conference on biometrics theory, applications and systems (BTAS). IEEE","DOI":"10.1109\/BTAS.2018.8698544"},{"issue":"3","key":"7894_CR43","doi-asserted-by":"crossref","first-page":"1065","DOI":"10.1111\/1556-4029.14664","volume":"66","author":"V Prasad","year":"2021","unstructured":"Prasad V, Prasad L, Lukose S, Agarwal P (2021) Latent fingerprint development by using silver nanoparticles and silver nitrate-A comparative study. J Forensic Sci 66(3):1065\u20131074","journal-title":"J Forensic Sci"},{"issue":"5","key":"7894_CR44","first-page":"50","volume":"9","author":"J Prost","year":"2021","unstructured":"Prost J, Cihak-Bayr U, Neac\u015fu IA, Grundtner R, Pirker F, Vorlaufer G (2021) Semi-supervised classification of the state of operation in self-lubricating journal bearings using a random forest classifier. Lubricants (Basel, Switzerland) 9(5):50","journal-title":"Lubricants (Basel, Switzerland)"},{"key":"7894_CR45","doi-asserted-by":"publisher","DOI":"10.1111\/1556-4029.14908","author":"M Saguy","year":"2021","unstructured":"Saguy M, Almog J, Cohn D, Champod C (2021) Proactive forensic science in biometrics: novel materials for fingerprint spoofing. J Forensic Sci. https:\/\/doi.org\/10.1111\/1556-4029.14908","journal-title":"J Forensic Sci"},{"key":"7894_CR46","doi-asserted-by":"crossref","first-page":"982","DOI":"10.1109\/ACCESS.2014.2349879","volume":"2","author":"A Sankaran","year":"2014","unstructured":"Sankaran A, Vatsa M, Singh R (2014) Latent fingerprint matching: a survey. IEEE Access: Pract Innov Open Solut 2:982\u20131004","journal-title":"IEEE Access: Pract Innov Open Solut"},{"key":"7894_CR47","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.inffus.2016.05.002","volume":"34","author":"A Sankaran","year":"2017","unstructured":"Sankaran A, Jain A, Vashisth T, Vatsa M, Singh R (2017) Adaptive latent fingerprint segmentation using feature selection and random decision forest classification. Int J Inf Fusion 34:1\u201315","journal-title":"Int J Inf Fusion"},{"key":"7894_CR48","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","volume":"61","author":"J Schmidhuber","year":"2015","unstructured":"Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw Off J Int Neural Netw Soc 61:85\u2013117","journal-title":"Neural Netw Off J Int Neural Netw Soc"},{"issue":"07","key":"7894_CR49","doi-asserted-by":"crossref","first-page":"826","DOI":"10.1017\/ice.2018.97","volume":"39","author":"ES Shenoy","year":"2018","unstructured":"Shenoy ES, Rosenthal ES, Shao Y-P, Biswal S, Ghanta M, Ryan EE, Suslak D et al (2018) Real-time, automated detection of ventilator-associated events: avoiding missed detections, misclassifications, and false detections due to human error. Infect Control Hosp Epidemiol Off J Soc Hosp Epidemiol Am 39(07):826\u2013833","journal-title":"Infect Control Hosp Epidemiol Off J Soc Hosp Epidemiol Am"},{"issue":"4","key":"7894_CR50","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1109\/TPAMI.2015.2465960","volume":"38","author":"J Shi","year":"2016","unstructured":"Shi J, Yan Q, Xu L, Jia J (2016) Hierarchical image saliency detection on extended CSSD. IEEE Trans Pattern Anal Mach Intell 38(4):717\u2013729","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"2","key":"7894_CR51","first-page":"243","volume":"25","author":"SP Singh","year":"2021","unstructured":"Singh SP, Ayub S, Saini JP (2021) Analysis and comparison of normal and altered fingerprint using artificial neural networks. Int J Knowl Based Intell Eng Syst 25(2):243\u2013249","journal-title":"Int J Knowl Based Intell Eng Syst"},{"key":"7894_CR52","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.optlastec.2018.06.061","volume":"110","author":"S Sonali","year":"2019","unstructured":"Sonali S, Sahu AK, Singh S.P. Ghrera, Elhoseny M (2019) An approach for De-noising and contrast enhancement of retinal fundus image using CLAHE. Opt Laser Technol 110:87\u201398","journal-title":"Opt Laser Technol"},{"key":"7894_CR53","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1007\/978-3-030-23364-8_2","volume-title":"Segmentation and separation of overlapped latent fingerprints","author":"B Stojanovi\u0107","year":"2019","unstructured":"Stojanovi\u0107 B, Marques O, Ne\u0161kovi\u0107 A (2019) Latent fingerprint datasets. Segmentation and separation of overlapped latent fingerprints. Springer International Publishing, Cham, pp 9\u201320"},{"key":"7894_CR54","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1007\/978-3-030-23364-8_4","volume-title":"Segmentation and separation of overlapped latent fingerprints","author":"B Stojanovi\u0107","year":"2019","unstructured":"Stojanovi\u0107 B, Marques O, Ne\u0161kovi\u0107 A (2019) Machine learning based segmentation of overlapped latent fingerprints. Segmentation and separation of overlapped latent fingerprints. Springer International Publishing, Cham, pp 29\u201334"},{"key":"7894_CR55","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.eswa.2018.09.049","volume":"117","author":"PJ Sudharshan","year":"2019","unstructured":"Sudharshan PJ, Petitjean C, Spanhol F, Oliveira LE, Heutte L, Honeine P (2019) Multiple instance learning for histopathological breast cancer image classification. Expert Syst Appl 117:103\u2013111","journal-title":"Expert Syst Appl"},{"key":"7894_CR56","doi-asserted-by":"publisher","DOI":"10.25008\/ijadis.v2i1.1204","author":"ZR Tembusai","year":"2021","unstructured":"Tembusai ZR, Mawengkang H, Zarlis M (2021) K-nearest neighbor with K-fold cross validation and analytic hierarchy process on data classification. Int J Adv Data Inf Syst. https:\/\/doi.org\/10.25008\/ijadis.v2i1.1204","journal-title":"Int J Adv Data Inf Syst"},{"issue":"5\u20136","key":"7894_CR57","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/S1353-2561(02)00072-5","volume":"7","author":"AD Venosa","year":"2002","unstructured":"Venosa AD, King DW, Sorial GA (2002) The baffled flask test for dispersant effectiveness: a round robin evaluation of reproducibility and repeatability. Spill Sci Technol Bull 7(5\u20136):299\u2013308","journal-title":"Spill Sci Technol Bull"},{"issue":"16","key":"7894_CR58","doi-asserted-by":"crossref","first-page":"4473","DOI":"10.3390\/s20164473","volume":"20","author":"GC Wan","year":"2020","unstructured":"Wan GC, Li MM, Xu H, Kang WH, Rui JW, Tong MS (2020) XFinger-net: pixel-wise segmentation method for partially defective fingerprint based on attention gates and U-net. Sensors (Basel, Switzerland) 20(16):4473","journal-title":"Sensors (Basel, Switzerland)"},{"issue":"3","key":"7894_CR59","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1007\/s00521-018-3924-0","volume":"32","author":"S-H Wang","year":"2020","unstructured":"Wang S-H, Muhammad K, Hong J, Sangaiah AK, Zhang Y-D (2020) Alcoholism identification via convolutional neural network based on parametric ReLU, dropout, and batch normalization. Neural Comput Appl 32(3):665\u2013680","journal-title":"Neural Comput Appl"},{"issue":"35","key":"7894_CR60","doi-asserted-by":"crossref","first-page":"12188","DOI":"10.1039\/D1DT01510A","volume":"50","author":"H-J Wang","year":"2021","unstructured":"Wang H-J, Hou W-Y, Kang J, Zhai X-Y, Chen H-L, Hao Y-W, Wan G-Y (2021) The facile preparation of solid-state fluorescent carbon dots with a high fluorescence quantum yield and their application in rapid latent fingerprint detection. Dalton Trans (Cambridge, England: 2003) 50(35):12188\u201312196","journal-title":"Dalton Trans (Cambridge, England: 2003)"},{"issue":"5","key":"7894_CR61","doi-asserted-by":"crossref","first-page":"2667","DOI":"10.1007\/s11042-015-2570-0","volume":"75","author":"X Xu","year":"2016","unstructured":"Xu X, Mu N, Chen L, Zhang X (2016) Hierarchical salient object detection model using contrast-based saliency and color spatial distribution. Multimed Tools Appl 75(5):2667\u20132679","journal-title":"Multimed Tools Appl"},{"issue":"12","key":"7894_CR62","doi-asserted-by":"crossref","first-page":"2494","DOI":"10.1109\/TMM.2016.2602938","volume":"18","author":"Y Yan","year":"2016","unstructured":"Yan Y, Nie F, Li W, Gao C, Yang Y, Xu D (2016) Image classification by cross-media active learning with privileged information. IEEE Trans Multimed 18(12):2494\u20132502","journal-title":"IEEE Trans Multimed"},{"key":"7894_CR63","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.neunet.2021.04.003","volume":"141","author":"D Yang","year":"2021","unstructured":"Yang D, Karimi HR, Sun K (2021) Residual wide-kernel deep convolutional auto-encoder for intelligent rotating machinery fault diagnosis with limited samples. Neural Netw Off J Int Neural Netw Soc 141:133\u2013144","journal-title":"Neural Netw Off J Int Neural Netw Soc"},{"key":"7894_CR64","first-page":"1682","volume":"33","author":"X Yuan","year":"2019","unstructured":"Yuan X, Feng Z, Norton M, Li X (2019) Generalized batch normalization: towards accelerating deep neural networks. Proceedings of the ...AAAI conference on artificial intelligence. AAAI Conf Artif Intell 33:1682\u20131689","journal-title":"AAAI Conf Artif Intell"},{"issue":"1","key":"7894_CR65","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1097\/SCS.0000000000002285","volume":"27","author":"MJ Zdilla","year":"2016","unstructured":"Zdilla MJ, Hatfield SA, McLean KA, Cyrus LM, Laslo JM, Lambert HW (2016) Circularity, solidity, axes of a best fit ellipse, aspect ratio, and roundness of the foramen ovale: a morphometric analysis with neurosurgical considerations: a morphometric analysis with neurosurgical considerations. J Craniofac Surg 27(1):222\u2013228","journal-title":"J Craniofac Surg"},{"key":"7894_CR66","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.patrec.2021.09.013","volume":"152","author":"Y Zhang","year":"2021","unstructured":"Zhang Y, Gao C, Li Z, Lv Y, Zhu K (2021) A method of fingermark anti-counterfeiting for forensic document identification. Pattern Recogn Lett 152:86\u201392","journal-title":"Pattern Recogn Lett"},{"key":"7894_CR67","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1016\/j.neunet.2020.01.018","volume":"124","author":"D-X Zhou","year":"2020","unstructured":"Zhou D-X (2020) Theory of deep convolutional neural networks: downsampling. Neural Netw Off J Int Neural Netw Soc 124:319\u2013327","journal-title":"Neural Netw Off J Int Neural Netw Soc"},{"key":"7894_CR68","first-page":"1","volume":"2021","author":"H Zhou","year":"2021","unstructured":"Zhou H, Yu G (2021) Research on fast pedestrian detection algorithm based on autoencoding neural network and adaboost. Complexity 2021:1\u201317","journal-title":"Complexity"},{"issue":"5","key":"7894_CR69","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1007\/s00530-017-0583-4","volume":"24","author":"F Zhu","year":"2018","unstructured":"Zhu F, Kong X, Fu H, Tian Q (2018) A novel two-stream saliency image fusion CNN architecture for person re-identification. Multimedia Syst 24(5):569\u2013582","journal-title":"Multimedia Syst"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07894-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07894-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07894-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,28]],"date-time":"2023-02-28T20:27:59Z","timestamp":1677616079000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07894-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,22]]},"references-count":69,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2023,3]]}},"alternative-id":["7894"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07894-y","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,22]]},"assertion":[{"value":"6 April 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 September 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 November 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors find no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}