Biometric Trait: Offline Signature Identification and Verification based on Multimodal Fusion Techniques

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Sunil S Harakannanavar, Jayalaxmi H, Asha C N, Kabballi Prashanth, Priya Hudedavar


Biometrics refers to the process of identification of humans by their
characteristics or traits. Biometrics is used in computer science as a form of
identification and access control which is one of the most secure methods to
keep humans’ privacy. Biometric can be classified into two categories:
behavioral and physiological. Handwritten signature is amongst the first few
biometrics to be used even before the advent of computers. Offline Signature
verification is an authentication method that uses the dynamics of a person's
handwritten signature measure and analyses the physical activity of signing. In
this research we demonstrate to study about offline signature verification
system. There are 2 main steps: Training and Testing. In training the database
is read one at a time and it is preprocessed by denoising, skeleton
identification by converting black/white image format and then identifying
bounding box of actual signature image and cropping it. By applying Integer
Wavelet Transform followed by Discrete Cosine Transform followed by
Principal Component Analysis, then features such as geometric, statistical are
extracted, concatenated, and saved with class tag and train with Neural
Network and develop structure. In testing after reading case image,
preprocessing, and extracting feature we test using Neural Network and
display class of test case. The accuracy of each method is found better on local
database and graph is plotted.

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