{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T16:58:41Z","timestamp":1791565121720,"version":"4.3.4"},"reference-count":32,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T00:00:00Z","timestamp":1791417600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T00:00:00Z","timestamp":1791417600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Offline handwriting functions as a practical biometric sensing modality for identity verification and controlled stress testing of recognition pipelines, yet recovering writer-specific stroke appearance from only a few enrollment samples remains difficult. In trajectory-based rendering used to probe such sensing systems, stroke width is often fixed or only weakly adapted, so the sensed thickness cue fails to reflect individual writing habits. The key obstacle is an operational reliability imbalance: a learned width estimator and handcrafted geometric priors disagree in trustworthiness across writers and even along a single stroke, which makes a constant fusion rule brittle for few-shot personalization. We propose a confidence-guided dynamic fusion framework whose primary method is a deterministic confidence-to-\u03b2 map (not a learned gate): point-wise pseudo-label confidence from reference images sets per-point blending weights between learned and rule-based widths, with \u201creliability\u201d denoting this operational blending prior. On 73 HWDB writers under a strict writer-disjoint protocol with 240 adaptation runs, dynamic fusion reduces writer-level variance and cross-seed instability relative to fixed fusion while retaining competitive point-wise accuracy. A security-oriented sensing analysis further contrasts detector responses under generic versus writer-adapted rendering, showing that the framework can serve as a controllable probe of verification sensitivity. The method plugs into existing trajectory generators and supports reproducible evaluation of personalization fidelity and biometric sensing robustness.<\/jats:p>","DOI":"10.3390\/s26196355","type":"journal-article","created":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T16:43:38Z","timestamp":1791564218000},"page":"6355","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Reliability-Guided Dynamic Fusion for Few-Shot Writer-Specific Stroke-Width Estimation in Offline Handwriting Biometric Sensing"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-9987-8623","authenticated-orcid":false,"given":"Jihan","family":"Liu","sequence":"first","affiliation":[{"name":"College of Artificial Intelligence, Tianjin University of Science and Technology, Tianjin 300457, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,10,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Dai, G., Zhang, Y., Wang, Q., Du, Q., Yu, Z., Liu, Z., and Huang, S. (2023). Disentangling writer and character styles for handwriting generation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR52729.2023.00579"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"129969","DOI":"10.1016\/j.neucom.2025.129969","article-title":"One-shot handwriting imitation via self-supervised cross spatial transformer networks","volume":"642","author":"Zhao","year":"2025","journal-title":"Neurocomputing"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Mayr, M., Dreier, M., Kordon, F., Seuret, M., Z\u00f6llner, J., Wu, F., Maier, A., and Christlein, V. (2024). Zero-shot paragraph-level handwriting imitation with latent diffusion models. arXiv.","DOI":"10.1007\/s11263-025-02525-0"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Huang, T.L., Do-Tran, N.T., Le, N.H.L., Shuai, H.H., and Huang, C.C. (2026). DNA: Dual-branch network with adaptation for open-set online handwriting generation. arXiv.","DOI":"10.1109\/WACV61042.2026.00406"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"111357","DOI":"10.1016\/j.patcog.2025.111357","article-title":"A survey of handwriting synthesis from 2019 to 2024: A comprehensive review","volume":"162","author":"Diaz","year":"2025","journal-title":"Pattern Recognit."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1007\/978-981-97-9619-9_20","article-title":"Handwritten signature verification and forgery detection using deep learning","volume":"Volume 1193","author":"Tiwari","year":"2025","journal-title":"Advances in Data and Information Sciences"},{"key":"ref_7","unstructured":"Qin, Y., Dai, G., Zhang, Y., Han, Y., He, Q., and Huang, S. (2026). Towards human-like robot handwriting via contour-aware generation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE."},{"key":"ref_8","unstructured":"Ayyalasomayajula, K.R., Malmberg, F., and Brun, A. (2019). CalligraphyNet: Augmenting handwriting generation with quill based stroke width. Proceedings of the IEEE International Conference on Image Processing (ICIP), IEEE."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1322432.1322434","article-title":"2D euclidean distance transform algorithms: A comparative survey","volume":"40","author":"Fabbri","year":"2008","journal-title":"ACM Comput. Surv."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1145\/357994.358023","article-title":"A fast parallel algorithm for thinning digital patterns","volume":"27","author":"Zhang","year":"1984","journal-title":"Commun. ACM"},{"key":"ref_11","unstructured":"Liu, J., Yu, W., Zhao, Y., Wei, X., Bai, Y., Tang, S., and Wang, H. (2026, January 27\u201330). Few-shot stroke-width imitation for offline handwriting rendering in trajectory-based synthesis. Proceedings of the International Conference on Information and Communication Security (ICICS), Fukui, Japan."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1007\/s00530-010-0182-0","article-title":"Multimodal fusion for multimedia analysis: A survey","volume":"16","author":"Atrey","year":"2010","journal-title":"Multimed. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Fogel, S., Averbuch-Elor, H., Cohen, S., Mazor, S., and Litman, R. (2020). ScrabbleGAN: Semi-supervised varying length handwritten text generation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR42600.2020.00438"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Dai, G., Zhang, Y., Ke, Q., Guo, Q., and Huang, S. (2024). One-shot diffusion mimicker for handwritten text generation. arXiv.","DOI":"10.1007\/978-3-031-73636-0_24"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"934","DOI":"10.1109\/TPAMI.2024.3481154","article-title":"VATr++: Choose your words wisely for handwritten text generation","volume":"47","author":"Vanherle","year":"2025","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Gurav, A., Chanda, S., and Krishnan, N.C. (2024). HTLDIFF: Handwritten text line generation: A diffusion model perspective. Proceedings of the 2024 IEEE Thirteenth International Conference on Image Processing Theory, Tools and Applications (IPTA), IEEE.","DOI":"10.1109\/IPTA62886.2024.10755723"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Dai, G., Zhang, Y., Qin, Y., Guo, Q., Huang, S., and Yan, S. (2025). Beyond isolated words: Diffusion brush for handwritten text-line generation. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), IEEE.","DOI":"10.1109\/ICCV51701.2025.01771"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Graves, A. (2013). Generating sequences with recurrent neural networks. arXiv.","DOI":"10.1007\/978-3-642-24797-2_3"},{"key":"ref_19","unstructured":"Finn, C., Abbeel, P., and Levine, S. (2017). Model-agnostic meta-learning for fast adaptation of deep networks. Proceedings of the 34th International Conference on Machine Learning (ICML), PMLR."},{"key":"ref_20","unstructured":"Houlsby, N., Giurgiu, A., Jastrzebski, S., Morrone, B., de Laroussilhe, Q., Gesmundo, A., Attariyan, M., and Gelly, S. Parameter-efficient transfer learning for NLP. Proceedings of the 36th International Conference on Machine Learning (ICML)."},{"key":"ref_21","unstructured":"Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W. (2022). LoRA: Low-rank adaptation of large language models. Proceedings of the International Conference on Learning Representations (ICLR), ICLR."},{"key":"ref_22","first-page":"99","article-title":"SCNN: Siamese convolutional neural network for handwritten signature verification","volume":"25","author":"Alharthi","year":"2025","journal-title":"Int. J. Comput. Sci. Netw. Secur. (IJCSNS)"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1504\/IJAPR.2023.130511","article-title":"A survey on different feature extraction methods for writer identification and verification","volume":"7","author":"Paul","year":"2023","journal-title":"Int. J. Appl. Pattern Recognit."},{"key":"ref_24","first-page":"250","article-title":"A unified benchmarking framework for offline handwritten signature verification using deep learning architectures","volume":"17","author":"Alreshidi","year":"2026","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"van Kempen, E., Karl, Z., Deamicis, R., and Chen, Q.A. (2024). Under pressure: Effectiveness and usability of the Apple Pencil as a biometric authentication tool. Proceedings of the Network and Distributed System Security (NDSS) Symposium, Internet Society.","DOI":"10.14722\/usec.2024.23056"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1109\/TPAMI.1986.4767851","article-title":"A computational approach to edge detection","volume":"PAMI-8","author":"Canny","year":"1986","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Perez, E., Strub, F., de Vries, H., Dumoulin, V., and Courville, A. (2018). FiLM: Visual reasoning with a general conditioning layer. Proceedings of the AAAI Conference on Artificial Intelligence, AAAI Press.","DOI":"10.1609\/aaai.v32i1.11671"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Liu, C.L., Yin, F., Wang, D.H., and Wang, Q.F. (2011). CASIA online and offline Chinese handwriting databases. Proceedings of the International Conference on Document Analysis and Recognition (ICDAR), IEEE.","DOI":"10.1109\/ICDAR.2011.17"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1111\/cgf.13861","article-title":"FontRNN: Generating Large-scale Chinese Fonts via Recurrent Neural Network","volume":"38","author":"Tang","year":"2019","journal-title":"Comput. Graph. Forum"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"17521","DOI":"10.1007\/s11042-022-13797-w","article-title":"Image forgery detection: A survey of recent deep-learning approaches","volume":"82","author":"Zanardelli","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., Efros, A.A., Shechtman, E., and Wang, O. (2018). The unreasonable effectiveness of deep features as a perceptual metric. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR.2018.00068"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3554729","article-title":"Diffusion models: A comprehensive survey of methods and applications","volume":"56","author":"Yang","year":"2023","journal-title":"ACM Comput. 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