Hybrid Wavelets based Feature Vector Generation from Multidimensional Data set for On-line Handwritten Signature Recognition

On-line handwritten Signature is one of the important behavioural biometric trait. On-line signature have more information such as x, y, z variations, pressure levels, Azimuth and Altitude of pen tip, due to this better accuracy can be achieved when signatures are captured in real time with digitizer device. In this paper a technique based on Hybrid Wavelets to extract texture features of Dynamic Handwritten (On-line) signature is proposed. Hybrid wavelets are flexible and combine the advantage of transforms and Multiresolution analysis. Proposed system uses the hybrid wavelets to generate the wavelet energy distribution of the pressure pattern of dynamic signatures, velocity magnitude, Azimuth & Altitude vectors. Hybrid Wavelet of Type I and Type II are used and their performance is compared. Hybrid Wavelets are found to give highest Performance Index of 83.96% for Azimuth and Altitude based feature vector.

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