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. 针对在线手写签名认证在小样本条件下存在特征表达受限、参数学习不稳定以及分类决策可靠性不足等问题，提出了一种融合轻量化表征、多尺度特征建模与概率判别机制的认证方法。该方法面向在线签名序列中噪声干扰、特征冗余以及动态信息利用不足等情况，首先对原始签名序列进行平滑处理，并结合特征重要性评估与主成分分析完成特征筛选与降维，构建同时包含全局统计属性和局部动态变化信息的输入表征。在特征提取阶段，采用Ghost特征映射对输入信息进行初始表征，并结合InceptionNext-TF模块与DASE模块开展多尺度深层特征建模，以刻画签名样本在不同尺度下的变化特征。在分类阶段，引入变分贝叶斯全连接层对输出权重进行分布化建模，并结合基于贝叶斯优化的自适应参数搜索策略，对相关超参数进行调整。基于MCYT-100和SVC-2004 Task2公开数据集的实验结果表明，在10-shot条件下，等错误率分别为1.46%和3.05%。
Fangjun et al. (Mon,) studied this question.