Signature recognition and identification is a vital behavioral biometric trait. Signature recognition system can be used to identify precisely user identity by making use of signature information such as x, y variations and pressure from a tablet PC. This makes way for using dynamic, i. e. , online handwritten signature based biometric system is more accurate than the static ones, hence can be useful for banking applications. In this paper new set of features are proposed for online or dynamic signature recognition. In this research, feature vector and their extraction mechanism is implemented using Webber Local Descriptor (WLD). Thus, helping many banking applications to identify forgery of signatures. The performance of proposed feature vector is further improved by provision of soft biometric traits of the signature.

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Biometrics Banking Applications Webber Local Descriptors Texture Features Online Signature