Classification Of Gender Using Global Level Features In Fingerprint For Malaysian Population

A new approach of algorithm based on the Mark Acree’s theory, focusing on fingerprint global extracted features is proposed and implemented for enhancing gender classification method. This proposed method can automatically execute the ridge calculation process from the 25mm2 fingerprint and enhance the forensic gender classification process. In this study, a relationship between fingerprint global features and a gender of person in Malaysian population is also explored, enhanced and improved by exploiting another five additional fingerprint features. A sample of 3000 fingerprints from 300 respondents of random selection are carefully taken before any relationship can be determined. For the classification part, five extracted features of the fingerprint are used which are Ridge Density (RD), Mean Ridge Count (RC), Ridge Thickness to Valley Thickness Ratio (RTVTR), White Lines Count (WLC) and Mean Pattern Types (PT). Two classification approaches which are the descriptive statistical and data mining are used in order to examine the classification of the gender by using the five extracted features. For data mining classification part, there are four popular machine learning classifiers used which are Bayesian Net.work (Bayes Net.), Multilayer Perceptron Neural Network (MLPNN), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM). These four classifiers are used in the data mining task with five test cases each in order to find the accuracies of the gender classification. The accuracy of the results from the proposed method is compared to the Acree Method is shown in terms of relative error. For statistical approach using Ridge Density (RD), the relative error is 3.7% for male respondent and 4.1% for female respondent. Meanwhile, the overall performance of the result from the proposed method achieved more than 90% classification rate for all the classifiers. SVM emerges as the best classifier for all the different cases in order to classify the gender using the results from the proposed method

SURVEY OF SOFT BIOMETRIC TECHNIQUES FOR GENDER IDENTIFICATION

Biometrics checks can be productively utilized for localization of intrusion in access control systems by utilizing soft computing frameworks.Biometrics procedures can be to a great extent separated into conventional and soft biometrics. The study presents a survey of the available softtechniques and comparison for gender identification from biometric techniques

False Identity Detection Using Complex Sentences

The use of faked identities is a current issue for both physical and online security. In this paper, we test the differences between subjects who report their true identity and the ones who give fake identity responding to control, simple, and complex questions. Asking complex questions is a new procedure for increasing liars' cognitive load, which is presented in this paper for the first time. The experiment consisted in an identity verification task, during which response time and errors were collected. Twenty participants were instructed to lie about their identity, whereas the other 20 were asked to respond truthfully. Different machine learning (ML) models were trained, reaching an accuracy level around 90-95% in distinguishing liars from truth tellers based on error rate and response time. Then, to evaluate the generalization and replicability of these models, a new sample of 10 participants were tested and classified, obtaining an accuracy between 80 and 90%. In short, results indicate that liars may be efficiently distinguished from truth tellers on the basis of their response times and errors to complex questions, with an adequate generalization accuracy of the classification models

Gender-From-Iris or Gender-From-Mascara?

Predicting a person's gender based on the iris texture has been explored by several researchers. This paper considers several dimensions of experimental work on this problem, including person-disjoint train and test, and the effect of cosmetics on eyelash occlusion and imperfect segmentation. We also consider the use of multi-layer perceptron and convolutional neural networks as classifiers, comparing the use of data-driven and hand-crafted features. Our results suggest that the gender-from-iris problem is more difficult than has so far been appreciated. Estimating accuracy using a mean of N person-disjoint train and test partitions, and considering the effect of makeup - a combination of experimental conditions not present in any previous work - we find a much weaker ability to predict gender-from-iris texture than has been suggested in previous work

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

CEAI: CCM based Email Authorship Identification Model

In this paper we present a model for email authorship identification (EAI) by employing a Cluster-based Classification (CCM) technique. Traditionally, stylometric features have been successfully employed in various authorship analysis tasks; we extend the traditional feature-set to include some more interesting and effective features for email authorship identification (e.g. the last punctuation mark used in an email, the tendency of an author to use capitalization at the start of an email, or the punctuation after a greeting or farewell). We also included Info Gain feature selection based content features. It is observed that the use of such features in the authorship identification process has a positive impact on the accuracy of the authorship identification task. We performed experiments to justify our arguments and compared the results with other base line models. Experimental results reveal that the proposed CCM-based email authorship identification model, along with the proposed feature set, outperforms the state-of-the-art support vector machine (SVM)-based models, as well as the models proposed by Iqbal et al. [1, 2]. The proposed model attains an accuracy rate of 94% for 10 authors, 89% for 25 authors, and 81% for 50 authors, respectively on Enron dataset, while 89.5% accuracy has been achieved on authors' constructed real email dataset. The results on Enron dataset have been achieved on quite a large number of authors as compared to the models proposed by Iqbal et al. [1, 2]

Gender Estimation from Fingerprints Using DWT and Entropy

Gender estimation from fingerprints have wide range of applications, especially in the field of forensics where identifying the gender of a criminal can reduce the list of suspects significantly. Although there have been quite a few research papers in the field of gender estimation from fingerprints most of those experiments used a lot of features but were only able to achieve poor classification results. That being the motivation behind the study we successfully proposed two different approaches for gender estimation from fingerprints and achieved high classification accuracy.;In this study we have developed two different approaches for gender estimation from fingerprints. The dataset used consists of 498 fingerprints of which 260 are male and 238 are female fingerprints. The first approach is based on wavelet analysis and uses features obtained from a six level discrete wavelet transform (DWT). Classification is performed using a decision stump classifier implemented in weka and was able to achieve a classification accuracy of 95.38% using the DWT approach. The second approach uses wavelet packet analysis and extracted the Shannon entropy and log-energy entropy from the coefficients of wavelet packet transform and provided a classification accuracy of 96.59% on the same dataset using decision stump classifier implemented in weka