Invariant Behavioural based Discrimination for Individual Representation

Wong Yee Leng, Siti Mariyam Shamsuddin, Nor Azman Ismail

Abstract


Writer identification based on cursive words is one of the areas in pattern recognition that has created a centre of attention by many researchers to work in. Recently, its focal point is in forensic investigation and biometric application as such the writing style can be used as behavioural features for authenticating an author. In this study, a novel approach of presenting cursive features of authors is presented. The invariants-based discriminability of the features is proposed by discretizing the moment features of each writer using Biometric Invariant Discretization Cutting Point (BIDCP). BIDCP is introduced for features perseverance to obtain better individual representations and discriminations. Our experiments have revealed that by using the proposed method, the authorship identification based on cursive words is significantly increased with an average identification rate of 99.80%.

Keywords


authorship; behavioural biometric; data mining; identification; discretization

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DOI: http://doi.org/10.11591/ijece.v11i1.pp%25p
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