Gender classification from offline multi-script handwriting images using oriented Basic Image Features (oBIFs)

Gattal, Abdeljalil; Djeddi, Chawki; Siddiqi, Imran; Chibani, Youcef

doi:10.1016/j.eswa.2018.01.038

Cited by 56 publications

(35 citation statements)

References 30 publications

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“…In order to objectively compare the performance of writer identification and gender classification systems, a ICDAR2015 Competition on Signature Verification and Writer Identification for On-and Off-line Skilled Forgeries (SigWIcomp2015) using QUWI database was organized [2]. The competition received five writer identification tasks and eight gender classification tasks.…”

Section: Of 25mentioning

confidence: 99%

“…A system of classification of gender is proposed in Reference [2] based on the protocols used in three competitions, ICDAR 2013, ICDAR 2015 and ICFHR 2016. The most interesting aspect of these competitions was the use of a dataset with writing samples of the same person in Arabic and in English.…”

Section: Of 25mentioning

confidence: 99%

“…The winner of on-line signature verification was a commercial product that calculates more than 70,000 movement characteristics of each handwritten sample. The best writer identification system of the English text was a system [2] based on the combination of edge-hinge features; multi-scale features and edge-based directional features.…”

Section: Of 25mentioning

confidence: 99%

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Writer Identification Using Handwritten Cursive Texts and Single Character Words

et al. 2019

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One of the biometric methods in authentication systems is the writer verification/identification using password handwriting. The main objective of this paper is to present a robust writer verification system by using cursive texts as well as block letter words. To evaluate the system, two datasets have been used. One of them is called Secure Password DB 150, which is composed of 150 users with 18 samples of single character words per user. Another dataset is public and called IAM online handwriting database, and it is composed of 220 users of cursive text samples. Each sample has been defined by a set of features, composed of 67 geometrical, statistical, and temporal features. In order to get more discriminative information, two feature reduction methods have been applied, Fisher Score and Info Gain Attribute Evaluation. Finally, the classification system has been implemented by hold-out cross validation and k-folds cross validation strategies for three different classifiers, K-NN, Naïve Bayes and Bayes Net classifiers. Besides, it has been applied for verification and identification approaches. The best results of 95.38% correct classification are achieved by using the k-nearest neighbor classifier for single character DB. A feature reduction by Info Gain Attribute Evaluation improves the results for Naïve Bayes Classifier to 98.34% for IAM online handwriting DB. It is concluded that the set of features and its reduction are a strong selection for the based-password handwritten writer identification in comparison with the state-of-the-art.

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Section: Of 25mentioning

confidence: 99%

Section: Of 25mentioning

confidence: 99%

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Writer Identification Using Handwritten Cursive Texts and Single Character Words

et al. 2019

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“…SVM is used as a classifier by [25] for gender classification using oriented basic image features where SVM is used with kernel parameter [50] that is selected in the range[0, 100] while the soft margin parameter C that is selected as 10.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

“…), is encoded as a data point in n-dimensional space. Then SVM will give a hyper plane to classify the features into two classes (male and female) and for nonlinearly separable classes it uses either Radial Basis Function (RBF) [51] or a kernel function [25].…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

A Study on Various Techniques Involved in Gender Prediction System: A Comprehensive Review

Maken

Gupta

2019

Cybernetics and Information Technologies

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Predicting gender on the foundation of handwriting investigation is a very invoking research area. Handwriting analysis also has numerous applications. It is useful for forensic experts to investigate classes of writers. This prediction is executed fundamentally by two steps: feature extraction and classification. As a whole, prediction solely depends upon the feature extraction. Distinct and most varied features make the classification accurate. This paper describes the problem comprehensively along with its foundation. It presents a survey of the available methods to solve the discussed problem. A comparative analysis of the discussed methods of feature extraction and classification along with the available databases is also presented in this paper.

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Handwriting-Based Gender Classification Using End-to-End Deep Neural Networks

Illouz

David

Netanyahu

2018

Lecture Notes in Computer Science

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Handwriting-based gender classification is a well-researched problem that has been approached mainly by traditional machine learning techniques. In this paper, we propose a novel deep learning-based approach for this task. Specifically, we present a convolutional neural network (CNN), which performs automatic feature extraction from a given handwritten image, followed by classification of the writers gender. Also, we introduce a new dataset of labeled handwritten samples, in Hebrew and English, of 405 participants. Comparing the gender classification accuracy on this dataset against human examiners, our results show that the proposed deep learning-based approach is substantially more accurate than that of humans.

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Gender classification from offline multi-script handwriting images using oriented Basic Image Features (oBIFs)

Cited by 56 publications

References 30 publications

Writer Identification Using Handwritten Cursive Texts and Single Character Words

Writer Identification Using Handwritten Cursive Texts and Single Character Words

A Study on Various Techniques Involved in Gender Prediction System: A Comprehensive Review

Handwriting-Based Gender Classification Using End-to-End Deep Neural Networks

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