First investigation of feasibility of contact-less non-destructive optical sensors to detect, acquire and digitally process forensic handwriting based on pressure information

Kalbitz, Michael; Scheidat, Tobias; Vielhauer, Claus

doi:10.1109/iwbf.2016.7449692

Cited by 5 publications

(14 citation statements)

References 11 publications

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“…As related to the previous works of authors [3][4][5], this article combines and extends the state of the art in the following ways:…”

Section: Related Workmentioning

confidence: 86%

“…Therefore, to overcome some of the ESDA limitations, authors in [3] and this article earlier suggested usage of industrial surface examination devices to acquire forensic handwriting traces. These contact-less and non-destructive optical 2D/3D sensor devices provide intensity and topography information in the nanometre range in high-resolution quality and are based on the CWL technology.…”

Section: Related Workmentioning

confidence: 99%

“…Further, the application of a 2D/3D nanometre confocal laser scanning microscope is shown, for example for textile fibres by Arndt et al [22] and for firearm identification based on microscopic deformations on cartridge cases by Fischer and Vielhauer [23]. According to these reports, in context to other fields of crime scene forensics surface examination sensors are evaluated successfully, to date of publication [3], authors observed no published work on the examination of forensic handwriting using CWL sensors nor other similar devices for industrial surface examination. This was the motivation to propose a new method, with an original process chain composed of a global threshold segmentation, based on impressions left by different writing tools on copy paper in order to separate the foreground (handwriting trace) from the background (paper).…”

Section: Related Workmentioning

confidence: 99%

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Biometric authentication based on 2D/3D sensing of forensic handwriting traces

2017

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Handwriting represents an important modality in both biometric and forensic domains. while the first field appears to be a well-studied area with many approaches for acquisition, feature extraction and classification, the latter is rather novel with respect to digitised, automated methods. In prior works, authors of this article have shown that forensic handwriting traces can be processed by means of high-resolution 2D/3D surficial scanners. These devices allow the acquisition at a vertical resolution down to a few nanometres. Pre-processing methods have been identified, which allow for spatial segmentation of writing traces and for de-noising of 2D and 3D data, leading to a clear visual enhancement of the writing traces based on topography information. Further, authors have suggested a novel benchmarking method for forensic trace analysis based on feature sets originally suggested for biometric handwriting and comparing the forensic recognition performance by means of equal error rates, achieved from biometric sensors and forensic 2D/3D sensors in parallel. This article summarises and extents these works in two main aspects: on one side, it presents significantly extended experiments with respect to test set size and feature sets utilised for classification. On the other side, authors suggest new optimisation approaches to the segmentation algorithm.

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“…As related to the previous works of authors [3][4][5], this article combines and extends the state of the art in the following ways:…”

Section: Related Workmentioning

confidence: 86%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Biometric authentication based on 2D/3D sensing of forensic handwriting traces

2017

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“…Previous studies [37][38][39][40] have focused on time or stroke features of handwriting. A writer recognition system for touch-screen mobile devices was proposed in Reference [37] for non-Latin languages with a large set of characters.…”

Section: Of 25mentioning

confidence: 99%

“…Therefore, the authors avoid complex time-intensive algorithms like Multilayer Perceptron, Support Vector Machine or Hidden Markov Model. To recognize strokes in a character, a small number of prototype of stroke shapes is used with weighted Dynamic Time Warping and a Look -Up Table. To analyze the topographic information of pen pressure during the writing process is the central idea of the research [39].…”

Section: Of 25mentioning

confidence: 99%

Writer Identification Using Handwritten Cursive Texts and Single Character Words

et al. 2019

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One of the biometric methods in authentication systems is the writer verification/identification using password handwriting. The main objective of this paper is to present a robust writer verification system by using cursive texts as well as block letter words. To evaluate the system, two datasets have been used. One of them is called Secure Password DB 150, which is composed of 150 users with 18 samples of single character words per user. Another dataset is public and called IAM online handwriting database, and it is composed of 220 users of cursive text samples. Each sample has been defined by a set of features, composed of 67 geometrical, statistical, and temporal features. In order to get more discriminative information, two feature reduction methods have been applied, Fisher Score and Info Gain Attribute Evaluation. Finally, the classification system has been implemented by hold-out cross validation and k-folds cross validation strategies for three different classifiers, K-NN, Naïve Bayes and Bayes Net classifiers. Besides, it has been applied for verification and identification approaches. The best results of 95.38% correct classification are achieved by using the k-nearest neighbor classifier for single character DB. A feature reduction by Info Gain Attribute Evaluation improves the results for Naïve Bayes Classifier to 98.34% for IAM online handwriting DB. It is concluded that the set of features and its reduction are a strong selection for the based-password handwritten writer identification in comparison with the state-of-the-art.

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