Recognizing Signatures Using Normalized Generalization Neural Network

One of the most prevalent behavioral biometrics is the signature. In this paper, signatures are utilized in the case of recognition. Multiple contributions are provided here. Firstly, statistical analysis of efficiency is taken into consideration for the feature extraction. Secondly, a novel classifier is suggested. It is employed to recognize the signatures and it is called the Normalized Generalization Neural Network (NGNN). In terms of error rates, comparisons are established between different neural networks in the literature and the novel NGNN. The proposed NGNN consists of the input layer, normalization layer, Radial Basis Function (RBF) layer, and output layer. It can be considered as an enhanced or developed version of the Generalized Regression Neural Network (GRNN). A large number of signatures' attributes from the Biometric Ideal Test (BIT) database is utilized. That is, 1750 patterns of attributes are exploited. A significant improvement in the error rates over previous networks is achieved when using the novel NGNN. The Mean Absolute Error (MAE) has reached 0.028 and the Mean Square Error (MSE) has obtained 0.014. In addition, further experimental results on the BIT database showed better Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) of 0.002 and 0.119, respectively.