Handwriting Biometrics: Applications and Future Trends in e-Security and e-Health

Online handwritten analysis presents many applications in e-security, signature biometrics being the most popular but not the only one. Handwriting analysis also has an important set of applications in e-health. Both kinds of applications (e-security and e-health) have some unsolved questions and relations among them that should be addressed in the next years. We summarize the state of the art and applications based on handwriting signals. Later on, we focus on the main achievements and challenges that should be addressed by the scientific community, providing a guide for future research. Among all the points discussed in this article, we remark the importance of considering security, health, and metadata from a joint perspective. This is especially critical due to the risks inherent when using these behavioral signals

An improved multiple classifier combination scheme for pattern classification

Combining multiple classifiers are considered as a new direction in the pattern recognition to improve classification performance. The main problem of multiple classifier combination is that there is no standard guideline for constructing an accurate and diverse classifier ensemble. This is due to the difficulty in identifying the number of homogeneous classifiers and how to combine the classifier outputs. The most commonly used ensemble method is the random strategy while the majority voting technique is used as the combiner. However, the random strategy cannot determine the number of classifiers and the majority voting technique does not consider the strength of each classifier, thus resulting in low classification accuracy. In this study, an improved multiple classifier combination scheme is proposed. The ant system (AS) algorithm is used to partition feature set in developing feature subsets which represent the number of classifiers. A compactness measure is introduced as a parameter in constructing an accurate and diverse classifier ensemble. A weighted voting technique is used to combine the classifier outputs by considering the strength of the classifiers prior to voting. Experiments were performed using four base classifiers, which are Nearest Mean Classifier (NMC), Naive Bayes Classifier (NBC), k-Nearest Neighbour (k-NN) and Linear Discriminant Analysis (LDA) on benchmark datasets, to test the credibility of the proposed multiple classifier combination scheme. The average classification accuracy of the homogeneous NMC, NBC, k-NN and LDA ensembles are 97.91%, 98.06%, 98.09% and 98.12% respectively. The accuracies are higher than those obtained through the use of other approaches in developing multiple classifier combination. The proposed multiple classifier combination scheme will help to develop other multiple classifier combination for pattern recognition and classification

Graphonomics and your Brain on Art, Creativity and Innovation : Proceedings of the 19th International Graphonomics Conference (IGS 2019 – Your Brain on Art)

[Italiano]: “Grafonomia e cervello su arte, creatività e innovazione”. Un forum internazionale per discutere sui recenti progressi nell'interazione tra arti creative, neuroscienze, ingegneria, comunicazione, tecnologia, industria, istruzione, design, applicazioni forensi e mediche. I contributi hanno esaminato lo stato dell'arte, identificando sfide e opportunità, e hanno delineato le possibili linee di sviluppo di questo settore di ricerca. I temi affrontati includono: strategie integrate per la comprensione dei sistemi neurali, affettivi e cognitivi in ambienti realistici e complessi; individualità e differenziazione dal punto di vista neurale e comportamentale; neuroaesthetics (uso delle neuroscienze per spiegare e comprendere le esperienze estetiche a livello neurologico); creatività e innovazione; neuro-ingegneria e arte ispirata dal cervello, creatività e uso di dispositivi di mobile brain-body imaging (MoBI) indossabili; terapia basata su arte creativa; apprendimento informale; formazione; applicazioni forensi. / [English]: “Graphonomics and your brain on art, creativity and innovation”. A single track, international forum for discussion on recent advances at the intersection of the creative arts, neuroscience, engineering, media, technology, industry, education, design, forensics, and medicine. The contributions reviewed the state of the art, identified challenges and opportunities and created a roadmap for the field of graphonomics and your brain on art. The topics addressed include: integrative strategies for understanding neural, affective and cognitive systems in realistic, complex environments; neural and behavioral individuality and variation; neuroaesthetics (the use of neuroscience to explain and understand the aesthetic experiences at the neurological level); creativity and innovation; neuroengineering and brain-inspired art, creative concepts and wearable mobile brain-body imaging (MoBI) designs; creative art therapy; informal learning; education; forensics

Biometrics Writer Recognition for Arabic language: Analysis and Classification techniques using Subwords Features

Handwritten text in any language is believed to convey a great deal of information about writers’ personality and identity. Indeed, handwritten signature has long been accepted as an authentication of the writer’s physical stamp on financial and legal deals as well official/personal documents and works of art. Handwritten documents are frequently used as evidences in forensic tasks. Handwriting skills is learnt and developed from the early schooling stages. Research interest in behavioral biometrics was the main driving force behind the growth in research into Writer Identification (WI) from handwritten text, but recent rise in terrorism associated with extreme religious ideologies spreading primarily, but not exclusively, from the middle-east has led to a surge of interest in WI from handwritten text in Arabic and similar languages. This thesis is the main outcome of extensive research investigations conducted with the aim of developing an automatic identification of a person from handwritten Arabic text samples. My motivations and interests, as an Iraqi researcher, emanate from my multi-faceted desires to provide scientific support for my people in their fight against terrorism by providing forensic evidences, and as contribute to the ongoing digitization of the Iraqi National archive as well as the wealth of religious and historical archives in Iraq and the middle-east. Good knowledge of the underlying language is invaluable in this project. Despite the rising interest in this recognition modality worldwide, Arabic writer identification has not been addressed as extensively as Latin writer identification. However, in recent years some new Arabic writer identification approaches have been proposed some of which are reviewed in this thesis. Arabic is a cursive language when handwritten. This means that each and every writer in this language develops some unique features that could demonstrate writer’s habits and style. These habits and styles are considered as unique WI features and determining factors. Existing dominating approaches to WI are based on recognizing handwriting habits/styles are embedded in certain parts/components of the written texts. Although the appearance of these components within long text contain rich information and clues to writer identity, the most common approaches to WI in Arabic in the literature are based on features extracted from paragraph(s), line(s), word(s), character(s), and/or a part of a character. Generally, Arabic words are made up of one or more subwords at the end of each; there is a connected stroke with a certain style of which seem to be most representative of writers habits. Another feature of Arabic writing is to do with diacritics that are added to written words/subwords, to add meaning and pronunciation. Subwords are more frequent in written Arabic text and appear as part of several different words or as full individual words. Thus, we propose a new innovative approach based on a seemingly plausible hypothesis that subwords based WI yields significant increase in accuracy over existing approaches. The thesis most significant contributions can be summarized as follows: - Developed a high performing segmentation of scanned text images, that combines threshold based binarisation, morphological operation and active shape model. - Defined digital measures and formed a 15-dimensional feature vectors representations of subwords that implicitly cover its diacritics and strokes. A pilot study that incrementally added features according to writer discriminating power. This reduced subwords feature vector dimension to 8, two of which were modelled as time series. - For the dependent 8-dimensional WI scheme, we identify the best performing set of subwords (best 22 subwords out of 49 then followed by best 11 out of these 22 subwords). - We established the validity of our hypothesis for different versions of subwords based WI schemes by providing empirical evidence when testing on a number of existing text dependent and in text-dependent databases plus a simulated text-in text-dependent DB. The text-dependent scenario results exhibited possible present of the Doddington Zoo phenomena. - The final optimal subword based WI scheme, not only removes the need to include diacritics as part of the subword but also demonstrating that including diacritics within subwords impairs the WI discriminating power of subwords. This should not be taken to discredit research that are based on diacritics based WI. Also in this subword body (without diacritics) base WI scheme, resulted in eliminating the presence of Doddington Zoo effect. - Finally, a significant but un-intended consequence of using subwords for WI is that there is no difference between a text-independent scenario and text-dependent one. In fact, we shall demonstrate that the text-dependent database of the 27-words can be used to simulate the testing of the scheme for an in text-dependent database without the need to record such a DB. Finally, we discussed ways of optimising the performance of our last scheme by considering possible ways of complementing our scheme using the addition of various image texture analysis features to be extracted from subwords, lines, paragraphs or entire file of the scabbed image. These included LBP and Gabor Filter. We also suggested the possible addition of few more features