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

Maken, Payal; Gupta, Abhishek; Gupta, Manoj Kumar

doi:10.2478/cait-2019-0015

Cited by 12 publications

(7 citation statements)

References 43 publications

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“…Dargan and Kumar [26] identified the writer and gender prediction in Gurumukhi script using hybridization of classifiers and estimated accuracy of 87.76% and 90.57%, respectively. Maken and Gupta [27] demonstrated a technique for offline handwriting-based gender classification systems using area, perimeter, and slanteness features and logarithmic regression, KNN, and SVM as classification methods to achieve high performance of the system.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Features for Gender Classification System based on Pre-segmented Handwritten Gurumukhi Characters Using Machine Learning Algorithms

SINGLA

Maini

Kumar

2023

Preprint

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Identification of handwriting has been an evolving task for mitigating specific issues in the fields of machine learning and pattern recognition. In the fields of artificial intelligence and machine learning, a gender classification method based on one or more features has emerged as a promising idea for addressing issues such as criminal, forensic, and suspected examinations. Predicting an individual's age, gender, and handedness based on their handwriting is one of the more challenging offline handwriting applications. The authors have presented a novel approach to classifying gender as male or female based on pre-segmented Gurumukhi (Punjabi) characters using feature extraction methods and classification algorithms such as K-NN, random forest, SVM, and MLPs in this proposed work. On a dataset of about 72,000 Gurumukhi characters, we extracted diagonal, transition, zoning, and peak extent based features (which consists of 280 writers with 150 males and 130 females). Our results show that by using a hybridization of multiple feature extraction techniques, the random forest classifier achieves higher accuracy than other classifiers.

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Section: Related Workmentioning

confidence: 99%

Hybrid Features for Gender Classification System based on Pre-segmented Handwritten Gurumukhi Characters Using Machine Learning Algorithms

SINGLA

Maini

Kumar

2023

Preprint

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“…Feature extraction and classifier training are the main foundations of these techniques. The literature research demonstrated that machine learning and deep learning methods had been successfully employed for classifying a writer's demographics [16]- [18], [22]. Despite their effectiveness, a fundamental problem in these deep learning-based systems is the availability of massive training data sets and powerful computing resources.…”

Section: Issn: 2302-9285 mentioning

confidence: 99%

“…A new method based on hinge feature and cloud of line distribution (COLD) is proposed by Gattal et al [17] to be used to identify the gender from handwriting. A number of studies mentioned in [18]- [20] validated the relationship between gender and handwriting. In these studies, a comparative analysis of the mentioned methods of feature extraction and classification, as well as the accessible datasets, is also presented.…”

Section: Introductionmentioning

confidence: 99%

Local binary pattern and its derivatives to handwriting-based gender classification

Abbas,

Gattal,

Menassel

2023

Bulletin EEI

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Several studies by psychologists and computer scientists have verified the link between handwriting and writer gender. The texture of the writing image is a major indicator of whether it is male or female writing. This paper conducts a comparison analysis to examine the effectiveness of various local binary patterns (LBPs) techniques in detecting gender from scanned images of handwriting. We study different LBP variants, including complete local binary pattern (CLBP), local ternary pattern (LTP), local configuration pattern (LCP), rotated local binary pattern (RLBP), local binary pattern variance (LBPV), and multi-scale local binary pattern (MLBP), as features for representing handwriting images. A support vector machine (SVM) is trained using features from male and female writing. The method achieves encouraging classification rates of 76.68 when tested on subsets of the Qatar University writer identification (QUWI) dataset containing English and Arabic writing samples when using the experimental protocols of the International Conference on Document Analysis and Recognition (ICDAR) 2013 gender classification competitions.

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“…The monitoring of traffic, humans, and any object can be done by such imaging. Many other industrial applications like speed determination, face detection [17], object recognition [54], object identification, document handling, character recognition [8], handwriting recognition [68], texture analysis [100], etc. are based on imaging in the visible spectrum.…”

Section: Imaging Techniques For Image Processingmentioning

confidence: 99%

Current research opportunities of image processing and computer vision

Gupta

2019

csci

Self Cite

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Image processing and computer vision is an important and essential area in today’s scenario. Several problems can be solved through computer vision techniques. There are a large number of challenges and opportunities which require skills in the field of computer vision to address them. Computer vision applications cover each band of the electromagnetic spectrum and there are numerous applications in every band. This article is targeted to the research students, scholars and researchers who are interested to solve the problems in the field of image processing and computer vision. It addresses the opportunities and current trends of computer vision applications in all emerging domains. The research needs are identified through available literature survey and classified in the corresponding domains. The possible exemplary images are collected from the different repositories available for research and shown in this paper. The opportunities mentioned in this paper are explained through the images so that a naive researcher can understand it well before proceeding to solve the corresponding problems. The databases mentioned in this article could be useful for researchers who are interested in further solving the problem. The motivation of the article is to expose the current opportunities in the field of image processing and computer vision along with corresponding repositories. Interested researchers who are working in the field can choose a problem through this article and can get the experimental images through the cited references for working further.

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

Cited by 12 publications

References 43 publications

Hybrid Features for Gender Classification System based on Pre-segmented Handwritten Gurumukhi Characters Using Machine Learning Algorithms

Hybrid Features for Gender Classification System based on Pre-segmented Handwritten Gurumukhi Characters Using Machine Learning Algorithms

Local binary pattern and its derivatives to handwriting-based gender classification

Current research opportunities of image processing and computer vision

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