论文标题
对独立于作家的离线手写签名验证的特征选择和转移学习的调查
An Investigation of Feature Selection and Transfer Learning for Writer-Independent Offline Handwritten Signature Verification
论文作者
论文摘要
Signet是用于手写签名验证（HSV）的功能表示的最先进模型的状态。该表示基于深度卷积神经网络（DCNN），并包含2048个维度。当二分法转化（DT）产生的差异空间（与作者独立的方法（WI）方法相关）时，这些特征可能包括冗余信息。本文使用二进制粒子群优化（BPSO）在包装模式下进行特征选择时，研究了过度拟合的存在。我们提出了一种基于全球验证策略的方法，该方法具有外部存档，以控制搜索最判别表示时过度拟合的方法。此外，还进行了调查，以评估转移学习环境中所选特征的使用。该分析是根据Cedar，MCYT和GPDS数据集的与作者无关的方法进行的。实验结果表明，当使用具有外部档案的全局验证策略时，在优化过程中未使用验证时，过度适应。同样，可以在传输学习环境中使用功能选择后产生的空间。
SigNet is a state of the art model for feature representation used for handwritten signature verification (HSV). This representation is based on a Deep Convolutional Neural Network (DCNN) and contains 2048 dimensions. When transposed to a dissimilarity space generated by the dichotomy transformation (DT), related to the writer-independent (WI) approach, these features may include redundant information. This paper investigates the presence of overfitting when using Binary Particle Swarm Optimization (BPSO) to perform the feature selection in a wrapper mode. We proposed a method based on a global validation strategy with an external archive to control overfitting during the search for the most discriminant representation. Moreover, an investigation is also carried out to evaluate the use of the selected features in a transfer learning context. The analysis is carried out on a writer-independent approach on the CEDAR, MCYT and GPDS datasets. The experimental results showed the presence of overfitting when no validation is used during the optimization process and the improvement when the global validation strategy with an external archive is used. Also, the space generated after feature selection can be used in a transfer learning context.
