  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.
