Biometric Signature Processing & Recognition Using Radial Basis Function Network
This work addresses signature verification for authentication applications, but it appears incremental as it applies an existing neural network method to this domain.
The paper tackled the problem of automatic signature recognition by proposing a method using a Radial Basis Function Network, achieving a recognition rate of approximately 80% on a dataset of 200 samples.
Automatic recognition of signature is a challenging problem which has received much attention during recent years due to its many applications in different fields. Signature has been used for long time for verification and authentication purpose. Earlier methods were manual but nowadays they are getting digitized. This paper provides an efficient method to signature recognition using Radial Basis Function Network. The network is trained with sample images in database. Feature extraction is performed before using them for training. For testing purpose, an image is made to undergo rotation-translation-scaling correction and then given to network. The network successfully identifies the original image and gives correct output for stored database images also. The method provides recognition rate of approximately 80% for 200 samples.