Book ChapterDOI
Writer’s Gender Classification Using HOG and LBP Features
Nesrine Bouadjenek,Hassiba Nemmour,Youcef Chibani +2 more
- pp 317-325
TLDR
This work proposes a system for writer’s gender classification that is based on local textural and gradient features, which are successful in various pattern recognition applications.Abstract:
The gender identification in handwritten documents becomes to gain importance for various writer authentication purposes. It provides information for anonymous documents for which we need to know if they were written by a Man or a Woman. In this work, we propose a system for writer’s gender classification that is based on local textural and gradient features. Especially our proposed features are Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP), which are successful in various pattern recognition applications. The classification step is achieved by SVM classifier. The results obtained on samples extracted from IAM dataset showed that the proposed features provide quite promising results.read more
Citations
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Journal ArticleDOI
Gender and Handedness Prediction from Offline Handwriting Using Convolutional Neural Networks
TL;DR: This work describes an experimental study on the suitability of deep neural networks to three automatic demographic problems: gender, handedness, and combined gender-and-handedness classifications, respectively, carried out on two public handwriting databases.
Journal ArticleDOI
A method for automatic classification of gender based on text- independent handwriting
Payal Maken,Abhishek Gupta +1 more
TL;DR: This research work predicts gender from handwriting using the landmarks of differences between the two genders using the shape or visual appearance of the handwriting for extracting features ofThe handwriting such as slanteness (direction), area, area (no of pixels occupied by text), perimeter (length of edges), etc.
Journal ArticleDOI
Handwriting-based gender and handedness classification using convolutional neural networks
TL;DR: In this article, the ability and capacity of deep CNNs in automatic classification of two handwriting based demographical problems, i.e. gender and handedness classification, have been examined by using advanced CNNs; DenseNet201, InceptionV3, and Xception.
Journal ArticleDOI
A Study on Various Techniques Involved in Gender Prediction System: A Comprehensive Review
TL;DR: A survey of the available methods to solve the problem of predicting gender on the foundation of handwriting investigation and a comparative analysis of the discussed methods along with the available databases are presented.
Proceedings ArticleDOI
Fuzzy Integral for Combining SVM-Based Handwritten Soft-Biometrics Prediction
TL;DR: This work addresses soft-biometrics prediction from handwriting analysis, which aims to predict the writer's gender, age range and handedness, using three SVM predictors associated each to a specific data feature to aggregate a robust prediction.
References
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Proceedings ArticleDOI
Histograms of oriented gradients for human detection
Navneet Dalal,Bill Triggs +1 more
TL;DR: It is shown experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection, and the influence of each stage of the computation on performance is studied.
Journal ArticleDOI
A Tutorial on Support Vector Machines for Pattern Recognition
TL;DR: There are several arguments which support the observed high accuracy of SVMs, which are reviewed and numerous examples and proofs of most of the key theorems are given.
Journal ArticleDOI
A comparative study of texture measures with classification based on featured distributions
TL;DR: This paper evaluates the performance both of some texture measures which have been successfully used in various applications and of some new promising approaches proposed recently.
Journal ArticleDOI
Off-line signature verification based on grey level information using texture features
TL;DR: A method for conducting off-line handwritten signature verification works at the global image level and measures the grey level variations in the image using statistical texture features using the co-occurrence matrix and local binary pattern.
Journal ArticleDOI
Explaining gender differences in crime and violence: The importance of social cognitive skills
TL;DR: Gender differences in the development of social cognition may help to explain gender differences in crime and violence as mentioned in this paper, and it is not necessarily suggested that deficiencies in cognitive capabilities cause crime, but rather that certain ways of processing social information and certain social cognitive memory structures help to protect the individual from personal, social, environmental, or situational pressures towards criminal behavior.