Comparative Analysis of Radial Basis Method with Backpropagation for Signature Identification

TSignatures are one of the most frequently used biometric authentication systems, they cannot be stolen or loaned to others. Signature recognition can be done by eye, but signature identification using this method can be fooled by someone's exper-tise in forging signatures. The absence of special characteristics of a signature that makes it difficult for people to forge it. Human physical limitations (fatigue, inaccuracy and impaired vision) can affect the interpretation of a signature. Therefore, automatic signature identification can assist forensic experts in processing and analyzing signatures. This study compares the backpropagation and radial basis artificial neural network methods. The data used are 96 images, consisting of 84 training images and 12 test images. Feature extraction used is Fast Fourier Transform, which is scanned horizontally and vertically. The backpropagation architecture uses traingdx learning, the variable rate of learning is 0.1, the ratio of increasing the learning rate is 1.6, the ratio is decreasing the learning rate is 0.5, and the momentum is 0.3. The first hidden layer is 48, the second hidden layer is 24 and the output is 12. The identification accuracy using the backpropagation method is 91.67%. The architecture of the radial basis method uses a data spread width of 1, epoh 10.000, goal 1e-2. The identification accuracy using the radial basis method is 66.67%. This research proves the backpropagation method is more accurate than the radial basis, if it is used to identify biometric systems with signature specimens.