Ghost Multi-scale Bayesian Network for Online Handwritten Signature Verification
沈阳建筑大学, 计算机科学与工程学院, 辽宁 110168,
| Abstract: | For the problems of limited feature representation, unstable parameter learning, and insufficient reliability of classification decisions in online handwritten signature verification under small-sample conditions, a verification method integrating lightweight representation, multi-scale feature modeling, and probabilistic discrimination is proposed. Aiming at noise interference, feature redundancy, and insufficient utilization of dynamic information in online signature sequences, the method first performs smoothing on the original signature sequences, and then combines feature importance evaluation with Principal Component Analysis to conduct feature selection and dimensionality reduction, thereby constructing an input representation that contains both global statistical attributes and local dynamic variation information. In the feature extraction stage, Ghost feature mapping is adopted for the initial representation of input information, and the InceptionNext-TF module together with the DASE module is used for multi-scale deep feature modeling to characterize variation patterns of signature samples at different scales. In the classification stage, a variational Bayesian fully connected layer is introduced to model the output weights in a distributional manner, and a Bayesian optimization-based adaptive parameter search strategy is further employed to adjust the relevant key hyperparameters. Experimental results on the public MCYT-100 and SVC-2004 Task2 datasets show that, under the 10-shot setting, the Equal Error Rates are 1.46% and 3.05%, respectively. |
| Keywords: | InceptionNext-TF; Variational Bayesian Fully Connected Layer; Bayesian Optimization-based Adaptive Parameter Search |
| DOI: | 10.57237/j.cst.2026.02.003 |
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Received: 25 March 2026
Accepted: 17 April 2026
Published Online: 27 April 2026
Funding
| 1. | 国家自然科学基金 (62073227) |
| 2. | 辽宁省科技厅项目 (2023JH2/101300212) |
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