<title>Retail applications of signature verification</title>

Abstract

The dramatic rise in identity theft, the ever pressing need to provide convenience in checkout services to attract and retain loyal customers, and the growing use of multi-function signature captures devices in the retail sector provides favorable conditions for the deployment of dynamic signature verification (DSV) in retail settings. We report on the development of a DSV system to meet the needs of the retail sector. We currently have a database of approximately 10,000 signatures collected from 600 subjects and forgers. Previous work at IBM on DSV has been merged and extended to achieve robust performance on pen position data available from commercial point of sale hardware, achieving equal error rates on skilled forgeries and authentic signatures of 1.5% to 4%.

Signature-Based Biometric Authentication

“…position, pressure, velocity, acceleration vs. time, etc.) are regarded as, mathematical time functions where the values directly constitute the feature set [36]. In the second group of dynamic signature verification, the techniques refer to several parameters as features.…”

“…• Speed: When a signature is captured with a digitizer, the pen motions (dynamics) are recorded. According to Zimmerman et al [36], when signing, the hand can operate in a rule known as ballistic movement, where the muscles are not controlled by sensual feedback. Ballistic motions are usually fast, practiced motions whose accurateness rises with speed [36].…”

Online Handwritten Signature Verification

“…34 Actually, signature is the most socially and legally accepted means for person 35 authentication and is therefore a modality confronted with high level attacks. Indeed, 36 when a person wants to bypass a system, he/she will forge the signature of another 37 person by trying to reproduce as close as possible the target signature. The online 38 context is favorable to identity verification because in order to produce a forgery, 39 an impostor has to reproduce more than the static image of the signature, namely a 40 personal and well anchored "gesture" of signing-more difficult to imitate than the 41 image of the signature.…”

“…42 On the other hand, even if a signature relies on a specific gesture, or a specific 43 motor model [24], it results in a strongly variable signal from one instance to the 44 next. Indeed, identity verification by an online signature still remains an enormous 45 challenge for research and evaluation, but signature, because of its wide use, remains 46 a potential field of promising applications [37,31,23,32,33]. 47 Some of the main problems are related to signature intraclass (intrapersonal) 48 variability and signature's time variability.…”

“…36 36 Number of strokes t5. 37 37-38 Mean of positive and negative velocity in x t5.38 39-40 Mean of positive and negative velocity in y 6.6.6.1 On the BIOMET Database…”