{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:27:51Z","timestamp":1740122871254,"version":"3.37.3"},"reference-count":68,"publisher":"Springer Science and Business Media LLC","issue":"13","license":[{"start":{"date-parts":[[2023,10,3]],"date-time":"2023-10-03T00:00:00Z","timestamp":1696291200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,10,3]],"date-time":"2023-10-03T00:00:00Z","timestamp":1696291200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001665","name":"Agence Nationale de la Recherche","doi-asserted-by":"publisher","award":["ANR-18-CE92-0024"],"award-info":[{"award-number":["ANR-18-CE92-0024"]}],"id":[{"id":"10.13039\/501100001665","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-023-15513-8","type":"journal-article","created":{"date-parts":[[2023,10,3]],"date-time":"2023-10-03T11:02:16Z","timestamp":1696330936000},"page":"38167-38192","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Unsupervised domain alignment of fingerprint denoising models using pseudo annotations"],"prefix":"10.1007","volume":"83","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2755-9416","authenticated-orcid":false,"given":"Indu","family":"Joshi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tushar","family":"Prakash","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rohit","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Antitza","family":"Dantcheva","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sumantra Dutta","family":"Roy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Prem Kumar","family":"Kalra","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,3]]},"reference":[{"key":"15513_CR1","doi-asserted-by":"crossref","unstructured":"Bousmalis K, Silberman N, Dohan D, Erhan D, Krishnan D (2017) Unsupervised pixel-level domain adaptation with generative adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pp 3722\u20133731","DOI":"10.1109\/CVPR.2017.18"},{"key":"15513_CR2","doi-asserted-by":"crossref","unstructured":"Cao K, Jain AK (2015) Latent orientation field estimation via convolutional neural network. In: Proceedings of the International Conference on Biometrics (ICB), pp 349\u2013356","DOI":"10.1109\/ICB.2015.7139060"},{"issue":"5","key":"15513_CR3","doi-asserted-by":"publisher","first-page":"1051","DOI":"10.1109\/TPAMI.2010.228","volume":"33","author":"R Cappelli","year":"2010","unstructured":"Cappelli R, Ferrara M, Maltoni D (2010) Fingerprint Indexing Based on Minutia Cylinder-Code. IEEE Trans Pattern Anal Mach Intell 33(5):1051\u20131057","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"12","key":"15513_CR4","doi-asserted-by":"publisher","first-page":"2128","DOI":"10.1109\/TPAMI.2010.52","volume":"32","author":"R Cappelli","year":"2010","unstructured":"Cappelli R, Ferrara M, Maltoni D (2010) Minutia Cylinder-Code: A New Representation and Matching Technique for Fingerprint Recognition. IEEE Trans Pattern Anal Mach Intell 32(12):2128\u20132141","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"15513_CR5","doi-asserted-by":"crossref","unstructured":"Chaidee W, Horapong K, Areekul V (2018) Filter design based on spectral dictionary for latent fingerprint pre-enhancement. In: Proceedings of the International Conference on Biometrics (ICB), pp 23\u201330","DOI":"10.1109\/ICB2018.2018.00015"},{"key":"15513_CR6","unstructured":"Chen C, Feng J, Zhou J (2016) Multi-scale dictionaries based fingerprint orientation field estimation. In: Proceedings of the International Conference on Biometrics (ICB), pp 1\u20138"},{"issue":"1","key":"15513_CR7","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1016\/j.patcog.2006.05.036","volume":"40","author":"S Chikkerur","year":"2007","unstructured":"Chikkerur S, Cartwright AN, Govindaraju V (2007) Fingerprint enhancement using STFT analysis. Pattern Recogn 40(1):198\u2013211","journal-title":"Pattern Recogn"},{"key":"15513_CR8","doi-asserted-by":"crossref","unstructured":"Choi Y, Choi M, Kim M, Ha JW, Kim S, Choo J (2018) Stargan: unified generative adversarial networks for multi-domain image-to-image translation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8789\u20138797","DOI":"10.1109\/CVPR.2018.00916"},{"key":"15513_CR9","doi-asserted-by":"crossref","unstructured":"Doersch C, Gupta A, Efros AA (2015) Unsupervised visual representation learning by context prediction. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pp 1422\u20131430","DOI":"10.1109\/ICCV.2015.167"},{"issue":"4","key":"15513_CR10","doi-asserted-by":"publisher","first-page":"925","DOI":"10.1109\/TPAMI.2012.155","volume":"35","author":"J Feng","year":"2013","unstructured":"Feng J, Zhou J, Jain AK (2013) Orientation Field Estimation for Latent Fingerprint Enhancement. IEEE Trans Pattern Anal Mach Intell 35(4):925\u2013940","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"6","key":"15513_CR11","doi-asserted-by":"publisher","first-page":"1727","DOI":"10.1109\/TIFS.2012.2215326","volume":"7","author":"M Ferrara","year":"2012","unstructured":"Ferrara M, Maltoni D, Cappelli R (2012) Noninvertible Minutia Cylinder-Code Representation. IEEE Trans Inform Forensics Secur 7(6):1727\u20131737","journal-title":"IEEE Trans Inform Forensics Secur"},{"key":"15513_CR12","doi-asserted-by":"crossref","unstructured":"Ghifary M, Kleijn WB, Zhang M, Balduzzi D (2015) Domain generalization for object recognition with multi-task autoencoders. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp 2551\u20132559","DOI":"10.1109\/ICCV.2015.293"},{"key":"15513_CR13","doi-asserted-by":"crossref","unstructured":"Ghifary M, Kleijn WB, Zhang M, Balduzzi D, Li W (2016) Deep reconstruction-classification networks for unsupervised domain adaptation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 597\u2013613","DOI":"10.1007\/978-3-319-46493-0_36"},{"key":"15513_CR14","unstructured":"Gidaris S, Singh P, Komodakis N (2018) Unsupervised representation learning by predicting image rotations. In: Proceedings of the International Conference on Learning Representations (ICLR)"},{"issue":"4","key":"15513_CR15","doi-asserted-by":"publisher","first-page":"2220","DOI":"10.1109\/TIP.2011.2170696","volume":"21","author":"C Gottschlich","year":"2011","unstructured":"Gottschlich C (2011) Curved-Region-Based Ridge Frequency Estimation and Curved Gabor Filters for Fingerprint Image Enhancement. IEEE Trans Image Process 21(4):2220\u20132227","journal-title":"IEEE Trans Image Process"},{"key":"15513_CR16","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1016\/j.ins.2020.01.031","volume":"530","author":"R Gupta","year":"2020","unstructured":"Gupta R, Khari M, Gupta D, Crespo RG (2020) Fingerprint Image Enhancement and Reconstruction using the Orientation and Phase Reconstruction. Inform Sci 530:201\u2013218","journal-title":"Inform Sci"},{"key":"15513_CR17","unstructured":"Hoffman J, Tzeng E, Park T, Zhu JY, Isola P, Saenko K, Efros A, Darrell T (2018) Cycada: cycle-consistent adversarial domain adaptation. In: Proceedings of the International Conference on Machine Learning (ICML), pp 1989\u20131998"},{"issue":"8","key":"15513_CR18","doi-asserted-by":"publisher","first-page":"777","DOI":"10.1109\/34.709565","volume":"20","author":"L Hong","year":"1998","unstructured":"Hong L, Wan Y, Jain A (1998) Fingerprint Image Enhancement: Algorithm and Performance Evaluation. IEEE Trans Pattern Anal Mach Intell 20(8):777\u2013789","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"15513_CR19","doi-asserted-by":"publisher","first-page":"96288","DOI":"10.1109\/ACCESS.2021.3093879","volume":"9","author":"K Horapong","year":"2021","unstructured":"Horapong K, Srisutheenon K, Areekul V (2021) Progressive and Corrective Feedback for Latent Fingerprint Enhancement using Boosted Spectral Filtering and Spectral Autoencoder. IEEE Access 9:96288\u201396308","journal-title":"IEEE Access"},{"issue":"2","key":"15513_CR20","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1016\/S0031-3203(02)00032-8","volume":"36","author":"CT Hsieh","year":"2003","unstructured":"Hsieh CT, Lai E, Wang YC (2003) An Effective Algorithm for Fingerprint Image Enhancement Based on Wavelet Transform. Pattern Recogn 36(2):303\u2013312","journal-title":"Pattern Recogn"},{"key":"15513_CR21","doi-asserted-by":"crossref","unstructured":"Jirachaweng S, Areekul V (2007) Fingerprint enhancement based on discrete cosine transform. In: Proceedings of the International Conference on Biometrics (ICB), pp 96\u2013105","DOI":"10.1007\/978-3-540-74549-5_11"},{"key":"15513_CR22","unstructured":"Joshi, I.: Advanced Deep Learning Techniques for Fingerprint Preprocessing. Ph.D. thesis, IIT Delhi (2021)"},{"key":"15513_CR23","doi-asserted-by":"crossref","unstructured":"Joshi I, Anand A, Dutta\u00a0Roy S, Kalra PK (2021) On training generative adversarial network for enhancement of latent fingerprints. In: AI and deep learning in biometric security, pp 51\u201379","DOI":"10.1201\/9781003003489-3"},{"key":"15513_CR24","doi-asserted-by":"crossref","unstructured":"Joshi I, Anand A, Vatsa M, Singh R, Dutta\u00a0Roy S, Kalra P (2019) Latent fingerprint enhancement using generative adversarial networks. In: IEEE Winter Conference on Applications of Computer Vision (WACV), pp 895\u2013903","DOI":"10.1109\/WACV.2019.00100"},{"key":"15513_CR25","doi-asserted-by":"crossref","unstructured":"Joshi I, Dhamija T, Kumar R, Dantcheva A, Dutta\u00a0Roy S, Kalra PK (2022) Cross-domain consistent fingerprint denoising. IEEE Sensors Letters","DOI":"10.1109\/LSENS.2022.3193924"},{"key":"15513_CR26","unstructured":"Joshi I, Grimmer M, Rathgeb C, Busch C, Bremond F, Dantcheva A (2022) Synthetic data in human analysis: a survey. arXiv:2208.09191"},{"key":"15513_CR27","doi-asserted-by":"crossref","unstructured":"Joshi I, Kothari R, Utkarsh A, Kurmi VK, Dantcheva A, Dutta\u00a0Roy S, Kalra PK (2021) Explainable fingerprint ROI segmentation using Monte Carlo dropout. In: IEEE Winter Conference on Applications of Computer Vision Workshops (WACVW), pp 60\u201369","DOI":"10.1109\/WACVW52041.2021.00011"},{"key":"15513_CR28","doi-asserted-by":"crossref","unstructured":"Joshi I, Prakash T, Jaiswal B, Kumar R, Dantcheva A, Dutta\u00a0Roy S, Kalra PK (2022) Context-aware restoration of noisy fingerprints. IEEE Sensors Letters","DOI":"10.1109\/LSENS.2022.3203787"},{"key":"15513_CR29","unstructured":"Joshi I, Utkarsh A, Kothari R, Kurmi VK, Dantcheva A, Dutta\u00a0Roy S, Kalra PK (2021 (accepted)) On estimating uncertainty of fingerprint enhancement models. In: Digital image enhancement and reconstruction ( (accepted))"},{"key":"15513_CR30","doi-asserted-by":"crossref","unstructured":"Joshi I, Utkarsh A, Kothari R, Kurmi VK, Dantcheva A, Dutta Roy S, Kalra PK (2021) Data uncertainty guided noise-aware preprocessing of fingerprints. In: International Joint Conference on Neural Networks (IJCNN), pp 1\u20138","DOI":"10.1109\/IJCNN52387.2021.9533528"},{"key":"15513_CR31","doi-asserted-by":"crossref","unstructured":"Joshi I, Utkarsh A, Kothari R, Kurmi VK, Dantcheva A, Dutta Roy S, Kalra PK (2021) Sensor-invariant fingerprint ROI segmentation using recurrent adversarial Learning. In: International Joint Conference on Neural Networks (IJCNN), pp 1\u20138","DOI":"10.1109\/IJCNN52387.2021.9533712"},{"key":"15513_CR32","doi-asserted-by":"crossref","unstructured":"Joshi I, Utkarsh A, Singh P, Dantcheva A, Dutta Roy S, Kalra PK (2022) On restoration of degraded fingerprints. Multimed Tool Appl 1\u201329","DOI":"10.1016\/B978-0-32-398370-9.00009-3"},{"issue":"25","key":"15513_CR33","doi-asserted-by":"publisher","first-page":"18569","DOI":"10.1007\/s11042-020-08750-8","volume":"79","author":"D Karabulut","year":"2020","unstructured":"Karabulut D, Tertychnyi P, Arslan HS, Ozcinar C, Nasrollahi K, Valls J, Vilaseca J, Moeslund TB, Anbarjafari G (2020) Cycle-Consistent Generative Adversarial Neural Networks based Low Quality Fingerprint Enhancement. Multimed Tool Appl 79(25):18569\u201318589","journal-title":"Multimed Tool Appl"},{"key":"15513_CR34","doi-asserted-by":"crossref","unstructured":"Li H, Pan SJ, Wang S, Kot AC (2018) Domain generalization with adversarial feature learning. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pp 5400\u20135409","DOI":"10.1109\/CVPR.2018.00566"},{"key":"15513_CR35","first-page":"52","volume":"60","author":"J Li","year":"2018","unstructured":"Li J, Feng J, Kuo CCJ (2018) Deep Convolutional Neural Network for Latent Fingerprint Enhancement. Signal Process: Image Commun 60:52\u201363","journal-title":"Signal Process: Image Commun"},{"key":"15513_CR36","doi-asserted-by":"publisher","first-page":"108405","DOI":"10.1016\/j.patcog.2021.108405","volume":"123","author":"Y Li","year":"2022","unstructured":"Li Y, Xia Q, Lee C, Kim S, Kim J (2022) A robust and efficient fingerprint image restoration method based on a phase-field model. Pattern Recogn 123:108405","journal-title":"Pattern Recogn"},{"key":"15513_CR37","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1016\/j.patcog.2017.02.012","volume":"67","author":"S Liu","year":"2017","unstructured":"Liu S, Liu M, Yang Z (2017) Sparse Coding Based Orientation Estimation for Latent Fingerprints. Pattern Recogn 67:164\u2013176","journal-title":"Pattern Recogn"},{"key":"15513_CR38","doi-asserted-by":"crossref","unstructured":"Liu YC, Yeh YY, Fu TC, Wang SD, Chiu WC, Wang YCF (2018) Detach and adapt: learning cross-domain disentangled deep representation. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pp 8867\u20138876","DOI":"10.1109\/CVPR.2018.00924"},{"key":"15513_CR39","unstructured":"Long M, Cao Y, Wang J, Jordan M (2015) Learning transferable features with deep adaptation networks. In: Proceedings of the International Conference on Machine Learning (ICML), pp 97\u2013105"},{"key":"15513_CR40","unstructured":"Long M, Cao Z, Wang J, Jordan MI (2017) Conditional adversarial domain adaptation pp 1647\u20131657"},{"key":"15513_CR41","doi-asserted-by":"crossref","unstructured":"Murez Z, Kolouri S, Kriegman D, Ramamoorthi R, Kim K (2018) Image to image translation for domain adaptation. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pp 4500\u20134509","DOI":"10.1109\/CVPR.2018.00473"},{"key":"15513_CR42","unstructured":"NIST: NBIS- NIST Biometric Image Software. http:\/\/biometrics.idealtest.org\/"},{"key":"15513_CR43","doi-asserted-by":"crossref","unstructured":"Pathak D, Krahenbuhl P, Donahue J, Darrell T, Efros AA (2016) Context encoders: feature learning by inpainting. In: Proc. IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pp 2536\u20132544","DOI":"10.1109\/CVPR.2016.278"},{"key":"15513_CR44","doi-asserted-by":"crossref","unstructured":"Puri C, Narang K, Tiwari A, Vatsa M, Singh R (2010) On analysis of rural and urban Indian fingerprint images. In: Proceedings of the international conference on ethics and policy of biometrics, pp 55\u201361","DOI":"10.1007\/978-3-642-12595-9_8"},{"key":"15513_CR45","doi-asserted-by":"crossref","unstructured":"Qian P, Li A, Liu M (2019) Latent fingerprint enhancement based on denseUNet. In: Proceedings of the International Conference on Biometrics (ICB), pp 1\u20136","DOI":"10.1109\/ICB45273.2019.8987279"},{"key":"15513_CR46","doi-asserted-by":"crossref","unstructured":"Qu Z, Liu J, Liu Y, Guan Q, Yang C, Zhang Y (2018) Orienet: a regression system for latent fingerprint orientation field extraction. In: Proceedings of the international conference on artificial neural networks, pp 436\u2013446","DOI":"10.1007\/978-3-030-01424-7_43"},{"key":"15513_CR47","doi-asserted-by":"crossref","unstructured":"Rama RK, Namboodiri AM (2011) Fingerprint enhancement using hierarchical Markov random fields. In: Proceedings of the IEEE International Joint Conference on Biometrics (IJCB), pp 1\u20138","DOI":"10.1109\/IJCB.2011.6117540"},{"key":"15513_CR48","doi-asserted-by":"crossref","unstructured":"Sahasrabudhe M, Namboodiri AM (2014) Fingerprint enhancement using unsupervised hierarchical feature learning. In: Proceedings of the IAPR- and ACM-sponsored Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP), pp 1\u20138","DOI":"10.1145\/2683483.2683485"},{"key":"15513_CR49","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1109\/ACCESS.2015.2428631","volume":"3","author":"A Sankaran","year":"2015","unstructured":"Sankaran A, Vatsa M, Singh R (2015) Multisensor Optical and Latent Fingerprint Database. IEEE Access 3:653\u2013665","journal-title":"IEEE Access"},{"key":"15513_CR50","doi-asserted-by":"crossref","unstructured":"Sankaranarayanan S, Balaji Y, Castillo CD, Chellappa R (2018) Generate to adapt: aligning domains using generative adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pp 8503\u20138512","DOI":"10.1109\/CVPR.2018.00887"},{"key":"15513_CR51","doi-asserted-by":"crossref","unstructured":"Schuch P, Schulz S, Busch C (2016) De-convolutional auto-encoder for enhancement of fingerprint samples. In: Proceedings of the International Conference on Image Processing Theory, Tools and Applications (IPTA), pp 1\u20137","DOI":"10.1109\/IPTA.2016.7821036"},{"key":"15513_CR52","doi-asserted-by":"crossref","unstructured":"Schuch P, Schulz S, Busch, C (2017) Survey on the impact of fingerprint image enhancement. IET Biometrics pp 102\u2013115","DOI":"10.1049\/iet-bmt.2016.0088"},{"key":"15513_CR53","doi-asserted-by":"crossref","unstructured":"Sharma RP, Dey S (2019) Two-stage quality adaptive fingerprint image enhancement using fuzzy c-means clustering based fingerprint quality analysis. Image Vis Comput 1\u201316","DOI":"10.1016\/j.imavis.2019.02.006"},{"key":"15513_CR54","doi-asserted-by":"publisher","first-page":"418","DOI":"10.1016\/j.neucom.2015.04.053","volume":"167","author":"K Singh","year":"2015","unstructured":"Singh K, Kapoor R, Nayar R (2015) Fingerprint denoising using ridge orientation based clustered dictionaries. Neurocomputing 167:418\u2013423","journal-title":"Neurocomputing"},{"key":"15513_CR55","doi-asserted-by":"crossref","unstructured":"Svoboda J, Monti F, Bronstein MM (2017) Generative convolutional networks for latent fingerprint reconstruction. In: Proceedings of the IEEE International Joint Conference on Biometrics (IJCB), pp 429\u2013436","DOI":"10.1109\/BTAS.2017.8272727"},{"key":"15513_CR56","doi-asserted-by":"crossref","unstructured":"Tiwari K, Gupta P (2014) Fingerprint quality of rural population and impact of multiple scanners on recognition. In: Chinese conference on biometric recognition, pp 199\u2013207","DOI":"10.1007\/978-3-319-12484-1_22"},{"key":"15513_CR57","doi-asserted-by":"crossref","unstructured":"Tzeng E, Hoffman J, Darrell T, Saenko K (2015) Simultaneous deep transfer across domains and tasks. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pp 4068\u20134076","DOI":"10.1109\/ICCV.2015.463"},{"key":"15513_CR58","doi-asserted-by":"crossref","unstructured":"Tzeng E, Hoffman J, Saenko K, Darrell T (2017) Adversarial discriminative domain adaptation. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pp 7167\u20137176","DOI":"10.1109\/CVPR.2017.316"},{"key":"15513_CR59","unstructured":"Tzeng E, Hoffman J, Zhang N, Saenko K, Darrell T (2014) Deep domain confusion: maximizing for domain invariance. arXiv:1412.3474"},{"key":"15513_CR60","doi-asserted-by":"crossref","unstructured":"Vatsa M, Singh R, Bharadwaj S, Bhatt H, Mashruwala R (2010) Analyzing fingerprints of Indian population using image quality: a UIDAI case study. In: Proceedings of the international workshop on emerging techniques and challenges for hand-based biometrics, pp 1\u20135","DOI":"10.1109\/ETCHB.2010.5559279"},{"key":"15513_CR61","doi-asserted-by":"crossref","unstructured":"Volpi R, Morerio P, Savarese S, Murino V. (2018) Adversarial feature augmentation for unsupervised domain adaptation. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pp 5495\u20135504","DOI":"10.1109\/CVPR.2018.00576"},{"issue":"3","key":"15513_CR62","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1016\/j.patrec.2007.10.004","volume":"29","author":"W Wang","year":"2008","unstructured":"Wang W, Li J, Huang F, Feng H (2008) Design and implementation of Log-gabor filter in fingerprint image enhancement. Pattern Recogn Lett 29(3):301\u2013308","journal-title":"Pattern Recogn Lett"},{"issue":"5","key":"15513_CR63","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3400066","volume":"11","author":"G Wilson","year":"2020","unstructured":"Wilson G, Cook DJ (2020) A Survey of Unsupervised Deep Domain Adaptation. ACM Trans Intell Syst Technol 11(5):1\u201346","journal-title":"ACM Trans Intell Syst Technol"},{"key":"15513_CR64","doi-asserted-by":"publisher","first-page":"107203","DOI":"10.1016\/j.patcog.2020.107203","volume":"101","author":"WJ Wong","year":"2020","unstructured":"Wong WJ, Lai SH (2020) Multi-Task CNN for Restoring Corrupted Fingerprint Images. Pattern Recogn 101:107203\u2013107213","journal-title":"Pattern Recogn"},{"key":"15513_CR65","doi-asserted-by":"crossref","unstructured":"Yan H, Ding Y, Li P, Wang Q, Xu Y, Zuo W (2017) Mind the class weight bias: weighted maximum mean discrepancy for unsupervised domain adaptation. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pp 2272\u20132281","DOI":"10.1109\/CVPR.2017.107"},{"issue":"5","key":"15513_CR66","doi-asserted-by":"publisher","first-page":"955","DOI":"10.1109\/TPAMI.2013.184","volume":"36","author":"X Yang","year":"2014","unstructured":"Yang X, Feng J, Zhou J (2014) Localized Dictionaries Based Orientation Field Estimation for Latent Fingerprints. IEEE Trans Pattern Anal Mach Intell 36(5):955\u2013969","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"6","key":"15513_CR67","doi-asserted-by":"publisher","first-page":"2047","DOI":"10.1109\/TPAMI.2019.2962476","volume":"43","author":"W Zhang","year":"2019","unstructured":"Zhang W, Xu D, Ouyang W, Li W (2019) Self-Paced Collaborative and Adversarial Network for Unsupervised Domain Adaptation. IEEE Trans Pattern Anal Mach Intell 43(6):2047\u20132061","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"15513_CR68","doi-asserted-by":"crossref","unstructured":"Zhu JY, Park T, Isola P, Efros AA (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp 2223\u20132232","DOI":"10.1109\/ICCV.2017.244"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-15513-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-15513-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-15513-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,3]],"date-time":"2024-04-03T10:29:39Z","timestamp":1712140179000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-15513-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,3]]},"references-count":68,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2024,4]]}},"alternative-id":["15513"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-15513-8","relation":{},"ISSN":["1573-7721"],"issn-type":[{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2023,10,3]]},"assertion":[{"value":"16 April 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 September 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 April 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 October 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"A. Dantcheva, one of the co-authors of this manuscript is a member of the editorial board of this journal.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}]}}