Abstract
A survey of the principal schemes in the literature suggested that a new way of addressing the problem of signature recognition be formulated in order to find a satisfactory solution for eliminating random forgeries. A fundamental problem in the field of off-line signature recognition is the lack of a pertinent shape representation or shape factor. This paper introduces a novel idea for a dynamic signature recognition system. An initial attempt is presented to demonstrate the data glove as an effective high-bandwidth data entry device for signature recognition. GloveSignature is a virtual-reality-based environment to support the signing process. The proposed approach retains the power to discriminate against forgeries. This paper extends the use of instrumented data gloves—gloves equipped with sensors for detecting finger bend and hand position and orientation for recognizing hand signatures. Several researchers have already explored the use of gloves in other application areas but using the gloves for the recognition of hand signatures has never been reported. An attempt is made in this research to explore the feasibility of using the 5th Glove in on-line signature recognition. Two hundred signatures were collected from 20 subjects, and features were extracted. We demonstrate the effectiveness of a hybrid technique, which is based on both the most discriminating eigenfeatures and the self-organizing maps (SOFMs) for signature recognition.