  Signature verification is an authentication technique that considers
handwritten signature as a biometric. From a biometric perspective this project
made use of automatic means through an integration of intelligent algorithms to
perform signal enhancement function such as filtering and smoothing for
optimization in conventional biometric systems. A handwritten signature is a 1D
Daubechies wavelet signal (db4) that utilizes Discrete Wavelet Transform (DWT)
and Discrete Cosine Transform (DCT) collectively to create a feature dataset
with d-dimensional space. In the proposed work the statistical features
characteristics are extracted from each particular signature per data source.
Two databases called Signature Verification Competition (SVC) 2004 database and
SUBCORPUS 100 MCYT Bimodal database are used to cooperate with the design
algorithm. Furthermore dimension reduction technique is applied to the large
feature vectors. A system model is trained and evaluated using the support
vector machine (SVM) classifier algorithm. Hence an equal error rate (EER) of
8.7 percent and an average correct verification rate of 91.3 percent
