Signature recognition through spectral analysis

Features such as shape, motion and pressure, minutiae details and timing, and transformation methods such as Hadamard and Walsh have been used in signature recognition with various degrees of success. One of the better studies was done by Sato and Kogure using nonlinear warping function. However, it is time consuming in terms of computer time and programming time. In this research, the signatures were normalized for size, orientation, etc. After normalization, the X and Y coordinates of each sampled point of a signature over time (to capture the dynamics of signature writing) were represented as a complex number and the set of complex numbers transformed into the frequency domain via the fast Fourier transform. A Gaussian probabilistic model was developed to screen and select from the large set of features (e.g. amplitude of each harmonics). The significant harmonics of the signature were sorted according to the chi-square value, which is equivalent to the signal-to-noise ratio. Fifteen harmonics with the largest signal-to-noise ratios from the true signatures were used in a discriminant analysis. A total of eight true signatures from a single person and eight each from nineteen forgers were used. It results in an error rate of 2.5%, with the normally more conservative jacknife procedure yielding the same small error rate.