Personal identification is one of the areas in pattern recognition that has created a center of attention by many researchers to work in. Recently, its focal point is in forensic investigation and biometric identification as such the physical (i.e., iris, fingerprint) and behavioural (i.e., signature) style can be used as biometric features for authenticating an individual. In this study, an improved approach of presenting biometric features of true individual from multi-form of biometric images is presented. The discriminability of the features is proposed by discretizing the extracted features of each person using improved Biometric Feature Discretization (BFD). BFD is introduced for features perseverance to obtain better individual representations and discriminations without the use of normalization. Our experiments have revealed that by using the proposed improved BFD in Multi-Biometric System, the individual identification is significantly increased with an average identification rate of 98%.
Over the past years, finger vein identification has gaining increasing attention in biometrics. It has many advantages as compared to other biometrics such as living-body identification, difficult to counterfeit because it resides underneath the finger skin and noninvasiveness. Finger vein feature extraction plays an important role in finger vein identification. The performance of finger vein identification is highly depending on the meaningful extracted features from feature extraction process. However, most of the works focus on how to extract the individual features and not presenting the individual characteristic of finger vein patterns with systematic representation. This paper proposed an improved scheme of finger vein feature extraction method by adopting discretization method. The extracted features will be represented systematically way in order to make classification task easier and increase the identification accuracy rate. The experimental result shows that the accuracy rate of identification of the proposed framework using Discretization is above 98.0%.
Writer identification based on cursive words is one of the extensive behavioural biometric that has involved many researchers to work in. Recently, its main idea is in forensic investigation and biometric analysis as such the handwriting style can be used as individual behavioural adaptation 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%.
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