Dynamic Signature Recognition using Hybrid Wavelets: Multidimensional Feature Vector Analysis using Hybrid Wavelets Type I & II

Biometrics, the word not only generates a spark of interest but it is also an important aspect to trace human expressions. Signature, an expression of this kind portrays a uniqueness which can be captured thus it becomes vital. Signature is a mark of authenticity which can be tampered by observing carefully. Dynamic Signature Recognition is one of the highly accurate biometric traits. Live signature of the person is captured, hence it is possible to have dynamic characteristics of signature for matching purpose. The signature captured by digitizer gives information about dynamic nature of signature and pressure applied while signing. This book is discussing use of Dynamic Signature Recognition using Hybrid wavelets. The technique is fast and gives good accuracy and tested in real time. Hybrid Wavelets are used for feature vector extraction, this is done by multiresolution analysis of the dynamic signatures. Wavelet energy based feature vector is generated and this is to be used for the matching of the signatures. The typical features set consists of x, y, z co-ordinates, Pressure, Azimuth and Altitude points. For performance evaluation we are using Conventional FAR-FRR analysis.