DISCRETIZATION OF INTEGRATED MOMENT INVARIANTS FOR WRITER IDENTIFICATION

Conservative regular moments have been proven to exhibit some shortcomings in the original formulations of moment functions in terms of scaling factor. Hence, an incorporated scaling factor of geometric functions into United Moment Invariant function is proposed for mining the feature of unconstrained words. Subsequently, the discrete proposed features undertake discretization procedure prior to classification for better feature representation and splendid classification accuracy. Collectively, discrete values are finite intervals in a continuous spectrum of values and well known to play important roles in data mining and knowledge discovery. Many induction algorithms found in the literature requires that training data contains only discrete features and some works better on discretized data; in particular rule based approaches like rough sets. Hence, in this study, an integrated scaling formulation of Aspect Scaling Invariant is presented in Writer Identification to hunt for the individuality perseverance. Successive exploration is executed to investigate for the suitability of discretization techniques in probing the issues of writer authorship. Mathematical proving and results of computer simulations are embraced to attest the feasibility of the proposed technique in Writer Identification. The results disclose that the proposed discretized invariants reveal 99% accuracy of classification by using 3520 training data and 880 testing data

Use of facial authentication in E-learning: a study of how it affects students in different Spanish-speaking areas.

Política de acceso abierto tomada de: https://v2.sherpa.ac.uk/id/publication/4573The authentication of students in E-learning is relevant to verify the assessment of distance learning students. Among diverse technologies for recognition, facial authentication (by means of biometrics) allows user identities to be corroborated and certified focusing on their facial physiological characteristics. The demand of students wishing to achieve admission to E-learning programs is actually high. Subsequently, it is essential for this type of education to be as respectable and recognised as any other. For this purpose, it would be essential to check the students’ identities while doing their homework using learning management systems such as Moodle platform. The main objective of this study is the analysis of student impressions concerning the development and implementation of facial verification for E-learning within the Moodle platform in different Spanish speaking areas like Spain and Latin America. A survey was carried out among the students after using the facial authentication tool within Moodle. The survey of 67 students from Masters produced high satisfaction scores about the acceptance of facial authentication as an improvement technique for distance education. Nevertheless, in general Spanish students reached lower average levels compared to Latin American students. These differences are statistically analysed to show their significance

Selecting Significant Features for Authorship Invarianceness in Writer Identification

Handwriting is individualistic. The uniqueness of shape and style of handwriting can be used to identify the significant features in authenticating the author of writing. Acquiring these significant features leads to an important research in Writer Identification domain where to find the unique features of individual which also known as Individuality of Handwriting. It relates to invarianceness of authorship where invarianceness between features for intraclass (same writer) is lower than inter-class (different writer). This paper discusses and reports the exploration of significant features for invarianceness of authorship from global shape features by using feature selection technique. The promising results show that the proposed method is worth to receive further exploration in identifying the handwritten authorship

Computationally Inexpensive Sequential Forward Floating Selection for Acquiring Significant Features for Authorship Invarianceness in Writer Identification

Handwriting is individualistic. The uniqueness of shape and style of handwriting can be used to identify the significant features in authenticating the author of writing. Acquiring these significant features leads to an important research in Writer Identification domain where to find the unique features of individual which also known as Individuality of Handwriting. This paper proposes an improved Sequential Forward Floating Selection method besides the exploration of significant features for invarianceness of authorship from global shape features by using various wrapper feature selection methods. The promising results show that the proposed method is worth to receive further exploration in identifying the handwritten authorship

Bio-Inspired Generalized Global Shape Approach for Writer Identification

Abstract—Writer identification is one of the areas in pattern recognition that attract many researchers to work in, particularly in forensic and biometric application, where the writing style can be used as biometric features for authenticating an identity. The challenging task in writer identification is the extraction of unique features, in which the individualistic of such handwriting styles can be adopted into bio-inspired generalized global shape for writer identification. In this paper, the feasibility of generalized global shape concept of complimentary binding in Artificial Immune System (AIS) for writer identification is explored. An experiment based on the proposed framework has been conducted to proof the validity and feasibility of the proposed approach for off-line writer identification

A New Swarm-Based Framework for Handwritten Authorship Identification in Forensic Document Analysis

Feature selection has become the focus of research area for a long time due to immense consumption of high-dimensional data. Originally, the purpose of feature selection is to select the minimally sized subset of features class distribution which is as close as possible to original class distribution. However in this chapter, feature selection is used to obtain the unique individual significant features which are proven very important in handwriting analysis of Writer Identification domain. Writer Identification is one of the areas in pattern recognition that have created a center of attention by many researchers to work in due to the extensive exchange of paper documents. Its principal point is in forensics and biometric application as such the writing style can be used as bio-metric features for authenticating the identity of a writer. Handwriting style is a personal to individual and it is implicitly represented by unique individual significant features that are hidden in individual’s handwriting. These unique features can be used to identify the handwritten authorship accordingly. The use of feature selection as one of the important machine learning task is often disregarded in Writer Identification domain, with only a handful of studies implemented feature selection phase. The key concern in Writer Identification is in acquiring the features reflecting the author of handwriting. Thus, it is an open question whether the extracted features are optimal or near-optimal to identify the author. Therefore, feature extraction and selection of the unique individual significant features are very important in order to identify the writer, moreover to improve the classification accuracy. It relates to invarianceness of authorship where invarianceness between features for intra-class (same writer) is lower than inter-class (different writer). Many researches have been done to develop algorithms for extracting good features that can reflect the authorship with good performance. This chapter instead focuses on identifying the unique individual significant features of word shape by using feature selection method prior the identification task. In this chapter, feature selection is explored in order to find the most unique individual significant features which are the unique features of individual’s writing. This chapter focuses on the integration of Swarm Optimized and Computationally Inexpensive Floating Selection (SOCIFS) feature selection technique into the proposed hybrid of Writer Identification framework 386 S.F. Pratama et al. and feature selection framework, namely Cheap Computational Cost Class-Specific Swarm Sequential Selection (C4S4). Experiments conducted to proof the validity and feasibility of the proposed framework using dataset from IAM Database by comparing the proposed framework to the existing Writer Identification framework and various feature selection techniques and frameworks yield satisfactory results. The results show the proposed framework produces the best result with 99.35% classification accuracy. The promising outcomes are opening the gate to future explorations in Writer Identification domain specifically and other domains generally

Multimodal biometrics scheme based on discretized eigen feature fusion for identical twins identification

The subject of twins multimodal biometrics identification (TMBI) has consistently been an interesting and also a valuable area of study. Considering high dependency and acceptance, TMBI greatly contributes to the domain of twins identification in biometrics traits. The variation of features resulting from the process of multimodal biometrics feature extraction determines the distinctive characteristics possessed by a twin. However, these features are deemed as inessential as they cause the increase in the search space size and also the difficulty in the generalization process. In this regard, the key challenge is to single out features that are deemed most salient with the ability to accurately recognize the twins using multimodal biometrics. In identification of twins, effective designs of methodology and fusion process are important in assuring its success. These processes could be used in the management and integration of vital information including highly selective biometrics characteristic possessed by any of the twins. In the multimodal biometrics twins identification domain, exemplification of the best features from multiple traits of twins and biometrics fusion process remain to be completely resolved. This research attempts to design a new scheme and more effective multimodal biometrics twins identification by introducing the Dis-Eigen feature-based fusion with the capacity in generating a uni-representation and distinctive features of numerous modalities of twins. First, Aspect United Moment Invariant (AUMI) was used as global feature in the extraction of features obtained from the twins handwritingfingerprint shape and style. Then, the feature-based fusion was examined in terms of its generalization. Next, to achieve better classification accuracy, the Dis-Eigen feature-based fusion algorithm was used. A total of eight distinctive classifiers were used in executing four different training and testing of environment settings. Accordingly, the most salient features of Dis-Eigen feature-based fusion were trained and tested to determine the accuracy of the classification, particularly in terms of performance. The results show that the identification of twins improved as the error of similarity for intra-class decreased while at the same time, the error of similarity for inter-class increased. Hence, with the application of diverse classifiers, the identification rate was improved reaching more than 93%. It can be concluded from the experimental outcomes that the proposed method using Receiver Operation Characteristics (ROC) considerably increases the twins handwriting-fingerprint identification process with 90.25% rate of identification when False Acceptance Rate (FAR) is at 0.01%. It is also indicated that 93.15% identification rate is achieved when FAR is at 0.5% and 98.69% when FAR is at 1.00%. The new proposed solution gives a promising alternative to twins identification application