Data glove is a new dimension in the field of virtual reality environments, initially designed to satisfy the stringent requirements of modern motion capture and animation professionals. In this study we try to shift the implementation of data glove from motion animation towards signature verification problem, making use of the offered multiple degrees of freedom for each finger and for the hand as well. We used an SVD-based technique to extract the feature values of different sensors' locating on corresponding fingers in the signing process and evaluated the results for writer authentication. The technique is tested with large number of authentic and forgery signatures using data gloves with 14, 5 and 4 sensor and shows a significant level of accuracy with 2.46~5.0% of EER.

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A Novel Approach to Dynamic Signature Verification Using Sensor-Based Data glove

Handwritten signature verification is a well-established and potential area of research with numerous applications such as commercial (e.g., credit card, bank check verification etc.), government (e.g., National ID card, Driver's license, passport control etc.) and forensic (e.g., corpse identification) application. In this paper, we propose a new approach to deal with the problem of handwritten signature verification and forgery detection using data glove. The technique is based on linearly projecting the glove signature into a low-dimensional space, through the singular value decomposition (SVD). The Euclidean distance between the different groups of singular values is used to measure the authenticity of the tried signatures. The reliability and efficiency of the proposed system against forgeries are tested and reported. A comparative analysis has also been shown for data gloves with 14, 5, and 4 sensors respectively

Dynamic Signature Verification Using Sensor Based Data Glove

This paper accounts the testing protocols used at the author's university. It examines the experiences of one particular study in dynamic signature verification. The paper also outlines some additions to the current testing methodologies.

Development of a biometric testing protocol for dynamic signature verification

Number plate is the unique identification of a vehicle. Automatic Number Plate Recognition (ANPR) is a system to capture the number plate of a moving vehicle and then to recognize it. Real-time recognition of number plate of moving vehicles is critical in numerous applications such as surveillance of moving vehicles, tracking and stopping vehicles associated with known suspects. It can be used not only for security and traffic management purposes but also for safety and general information gathering applications. Aiming at general ANPR's objectives, namely fast processing speed, high accuracy and low cost, many researches focus on recognizing the characters on number plate image quickly and accurately. The goal of ANPR research is to develop algorithms and methods that can recognize number plate accurately and quickly at anyone time, day or night, in almost any weather conditions. In this paper, the general process of image recognition system is extracted. This process includes preprocessing, feature extraction, classification, and optimization. Following this processing chain, each stage is discussed in detail and afew of well-known approaches used are represented. After analysis of the merits and pitfalls of existing systems, a combined recognition system is proposed for ANPR system. Using several efficient classifiers such as Template Matching and Support Vector Machines (SVMs), the combined system can improve the processing ability not only in accuracy but also in

/pdf/a-comparison-of-methods-for-character-recognition-of-car-28te936kf0.pdf

A Comparison of Methods for Character Recognition of Car Number Plates

Dynamic signature verification has many challenges associated with the creation of the impostor dataset. The literature discusses several ways of determining the impostor signature provider, but this takes a different approach - that of the opportunistic forger and his or her relationship to the genuine signature holder. This examines the accuracy with which an opportunistic forger assesses the various traits of the genuine signature, and whether the genuine signature holder believes that his or her signature is easy to forge.

Dynamic signature forgery and signature strength perception assessment

Signature verification is a natural and friendly approach in biometrics-based verification. As we know, the system can achieve a better performance if more training samples are collected. However, routinely signing the patterns is a boring and inconvenient process to obtain enough training samples at the initial enrolment. In this paper, we have proposed a multi-template matching approach to identify the individual via few training samples. Some experimental results were conducted to show the effectiveness of our proposed methods.

Chin-Chuan Han

Papers

An on-line signature verification system using multi-template matching approaches