Off-line handwritten signature recognition by wavelet entropy and neural network

Handwritten signatures are widely utilized as a form of personal recognition. However, they have the unfortunate shortcoming of being easily abused by those who would fake the identification or intent of an individual which might be very harmful. Therefore, the need for an automatic signature recognition system is crucial. In this paper, a signature recognition approach based on a probabilistic neural network (PNN) and wavelet transform average framing entropy (AFE) is proposed. The system was tested with a wavelet packet (WP) entropy denoted as a WP entropy neural network system (WPENN) and with a discrete wavelet transform (DWT) entropy denoted as a DWT entropy neural network system (DWENN). Our investigation was conducted over several wavelet families and different entropy types. Identification tasks, as well as verification tasks, were investigated for a comprehensive signature system study. Several other methods used in the literature were considered for comparison. Two databases were used for algorithm testing. The best recognition rate result was achieved by WPENN whereby the threshold entropy reached 92%

Evaluating current authentication methods: Prediction of a more suitable authentication approach for public interaction

The trend in information technology is towards achieving ubiquitous service rendering where barriers (geographical, time) in getting information related services will be eliminated. A good example of this is the ATM machines used by banks for public banking services, the likes in other sectors are currently on the way.This paper explores the user’s perception on the current identity authentication (Token-based and Knowledgebased) with a view to predict a more secured authentication for authentication in public places.A survey study is conducted so as to justify the claims of the previous authors on the need to migrate from the conventional knowledge-based and tokenbased authentication methods to a more secured biometric authentication approach which makes impossible user’s impersonation, and thus minimizing fraud, particularly in business transactions.Also, biometrical identification was also reviewed with respect to the all known biometric identifiers where human iris data was revealed to be the best human trait that can be used for identification/authentication purposes in public zone