This research introduces an innovative approach to explore the cognitive and biologically inspired underpinnings of feature vector splitting for analyzing the significance of different attributes in e-security biometric signature recognition applications. Departing from traditional methods of concatenating features into an extended set, we employ multiple splitting strategies, aligning with cognitive principles, to preserve control over the relative importance of each feature subset. Our methodology is applied to three diverse databases (MCYT100, MCYT300,and SVC) using two classifiers (vector quantization and dynamic time warping with one and five training samples). Experimentation demonstrates that the fusion of pressure data with spatial coordinates (x and y) consistently enhances performance. However, the inclusion of pen-tip angles in the same feature set yields mixed results, with performance improvements observed in select cases. This work delves into the cognitive aspects of feature fusion,shedding light on the cognitive relevance of feature vector splitting in e-security biometric applications.

翻译：本研究提出了一种创新方法，深入探索基于认知与生物启发机制的特征向量分割原理，以分析电子安全生物特征签名识别应用中不同属性的重要性。与传统将特征拼接为扩展集合的方法不同，我们采用多种分割策略（遵循认知原则）来保持对每个特征子集相对重要性的控制。该方法应用于三个不同数据库（MCYT100、MCYT300和SVC），并使用两种分类器（矢量量化及基于1个和5个训练样本的动态时间规整）。实验表明，将压力数据与空间坐标（x和y）融合能持续提升性能；然而，在相同特征集中包含笔尖角则产生混合结果，仅在特定案例中观察到性能改进。本研究深入探讨了特征融合的认知层面，阐明了特征向量分割在电子安全生物特征应用中的认知相关性。