Markov Model-Based Handwritten Signature Verification

Abstract

Biometric security devices are now permeating all facets of modern society. All manner of items including passports, driver's licences and laptops now incorporate some form of biometric data and/or authentication device. As handwritten signatures have long been considered the most natural method of verifying one's identity, it makes sense that pervasive computing environments try to capitalise on the use of automated Handwritten Signature Verification systems (HSV). This paper presents a HSV system that is based on a Hidden Markov Model (HMM) approach to representing and verifying the hand signature data. HMMs are naturally suited to modelling flowing entities such as signatures and speech. The resulting HSV system performs reasonably well with an overall error rate of 3.5% being reported in the best case experimental analysis.

Off-Line Handwritten Signature Recognition by Wavelet Entropy and Neural Network

“…A system for verifying handwritten signatures where various static and dynamic signature features are extracted and used as a pattern to train several network topologies is presented [11]. A signature verification system based on a Hidden Markov Model approach for verifying the hand signature data is presented in [12].…”

“…A similar result can be seen in [26], where a vector quantization (VQ) pattern recognition algorithm is also tested. Recent proposals show some improvements, by combining systems (VQ-DTW) in [26], by fusing local and global information [12], or by combining different system outputs [25]. In [26] was seen that DTW had performed well in the problem of measuring similarity when the signatures to be compared have different lengths.…”

Authentication System for Any Touch Devices Using DWT

“…HMMs are naturally suited to modelling flowing entities such as signatures and speech. The new version of the imputed system reported in the best case experimental analysis [4].…”

“…The proposed online signature verification system recognition [8] and off-line signature verification [3]. Using histograms for online signature verification was first suggested by Nelson etal [4]. They have also been used as part of the feature set in [1] and [5].…”

“…However, not even one of this has been directed to the context of authentication on mobile devices [3]. An example of finger drawn signatures on mobile devices work has addressed on-line signatures acquired using traditional digitizers in a controlled environment [4]. These are different from those acquired using mobile devices in a dynamic environments.…”