Determining the Personal identity based on Handwriting as a Biometric identification

This paper describes methods for off-line identification of the writer based on handwriting features. Different methods for extracting and combining features are reported in the literature for pattern recognition purposes. Many aspects have influence over the writer identification such as: symmetry, slant angle, percent of black and white pixels, height/width ratio of the letters, direction of the base line, the position of the horizontal and vertical lines in the segments, histograms, contour profiles, spots, etc. The method creates a feature-vector associated with a writing manner of an individual and computes the correlation measure to express the similarity with the previously stored handwritten samples of the Cyrillic letters. The presented system is based on image processing and pattern recognition methods. The approach analyses the handwriting as an image-texture, it is content independent and uses feature set based on the global statistical, structural and topological characteristics. An experiment was performed to discover the most reliable features that contribute to the identification of the writer. Handwritten biometric identification is applicable in many areas such as: security systems, forensics, financial etc

A Unified Multilingual Handwriting Recognition System using multigrams sub-lexical units

We address the design of a unified multilingual system for handwriting recognition. Most of multi- lingual systems rests on specialized models that are trained on a single language and one of them is selected at test time. While some recognition systems are based on a unified optical model, dealing with a unified language model remains a major issue, as traditional language models are generally trained on corpora composed of large word lexicons per language. Here, we bring a solution by con- sidering language models based on sub-lexical units, called multigrams. Dealing with multigrams strongly reduces the lexicon size and thus decreases the language model complexity. This makes pos- sible the design of an end-to-end unified multilingual recognition system where both a single optical model and a single language model are trained on all the languages. We discuss the impact of the language unification on each model and show that our system reaches state-of-the-art methods perfor- mance with a strong reduction of the complexity.Comment: preprin

Investigating the impact of combining handwritten signature and keyboard keystroke dynamics for gender prediction

© 2019 IEEE. The use of soft-biometric data as an auxiliary tool on user identification is already well known. Gender, handorientation and emotional state are some examples which can be called soft-biometrics. These soft-biometric data can be predicted directly from the biometric templates. It is very common to find researches using physiological modalities for soft-biometric prediction, but behavioural biometric is often not well explored for this context. Among the behavioural biometric modalities, keystroke dynamics and handwriting signature have been widely explored for user identification, including some soft-biometric predictions. However, in these modalities, the soft-biometric prediction is usually done in an individual way. In order to fill this space, this study aims to investigate whether the combination of those two biometric modalities can impact the performance of a soft-biometric data, gender prediction. The main aim is to assess the impact of combining data from two different biometric sources in gender prediction. Our findings indicated gains in terms of performance for gender prediction when combining these two biometric modalities, when compared to the individual ones