Biometrics is the utilization of biological characteristics (face, iris, fingerprint) or behavioral traits (signature, voice) for identity verification of an individual. Biometric authentication is gaining popularity as a more trustable alternative to password based security systems as it is relatively hard to be forgotten, stolen, or guessed. Signature is a behavioral biometric: it is not based on the physical properties, such as fingerprint or face, of the individual, but behavioral ones. As such, one’s signature may change over time and it is not nearly as unique or difficult to forge as iris patterns or fingerprints, however signature’s widespread acceptance by the public, make it more suitable for certain lower-security authentication needs. Signature verification is split into two according to the available data in the input. Off-line signature verification takes as input the image of a signature and is useful in automatic verification of signatures found on bank checks and documents. On-line signature verification uses signatures that are captured by pressure-sensitive tablets and could be used in real time applications like credit card transactions or resource accesses. In this work we present a complete system for on-line signature verification. During registration to system the user has to submit a number of reference signatures which are cross aligned to extract statistics describing the variation in the user’s signatures. A test signature’s authenticity is established by first aligning it with each reference signature of the claimed user, resulting in a number of dissimilarity scores: distances to nearest, farthest and template reference signatures. In previous systems, only one of these distances, typically the distance to the nearest reference signature or the distance to a template signature, was chosen, in an ad-hoc manner, to Key words: Support Vector Machine, Dynamic Time Warping, Extremum Matching, Principle Component Analysi
