The ultimate signature identifier?

Abstract

Biometric identification is the need of hour, as automatic recognition systems are such biometric techniques to look upon. Accurate automatic recognition systems are important for a wide range of applications such as banks, restricted areas, government classified areas etc. As traditional identity recognition methods such as pins, passwords etc suffer from some fattle flaws and are unable to satisfy the security requirements. The paper aims to consider a more reliable biometric feature, signature verification for the considering. The paper presents an experimental comparison of different signature verification methods. It can be effectively employed to reduce the risk of forgery by releasing the trouble of carrying ATM cards by the users, by employing a signature verification system. The paper compares four of the most extremely efficient methods used for signature verification like CDTW (Continuous Dynamic Time Warping), DTW (Dynamic Time Wrapping), Vector Quantization followed by HMM (Hidden Markov Model) employed for signature verification and the best method was stated. The paper also aims at improving the efficiency by integrating the methodologies introduced in the paper with each other. The methods were tested on synthetic and further applied on real time signature datasets. It is proved that superiority is achieved by combination of different methods.