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AGE AND GENDER DETECTION

December 2021

December 2021

Authors:

Sri Chandrasekharendra Saraswathi Viswa Mahavidyalaya University

The massive use of the social media and the huge number of messages that are shared on the internet,create a countless need to automatically detect the age and gender of the people who write the messages.Several types of platforms uses several ways for finding the truth about a message .Here we used a few machine learning algorithms to classify them and detect them.

The proposed age and gender methodology

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Age prediction

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Figures - uploaded by C K Gomathy

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Content uploaded by C K Gomathy

Content may be subject to copyright.

International Journal of Scientific Research in Engineering and Management (IJSREM)

Volume: 05 Issue: 10 | Oct - 2021 ISSN: 2582-3930

AGE AND GENDER DETECTION

DR.C.K.Gomathy , Mr.A.Lokesh ,Mr.CH.HARSHAVARDHAN REDDY, Mr.A.SAI KIRAN

Sri Chandrasekharendra Saraswathi Viswa Mahavidyalaya , Kanhipuram

ABSTRACT:

The massive use of the social media and the

huge number of messages that are shared on

the internet,create a countless need to

automatically detect the age and gender of

the people who write the messages.Several

types of platforms uses several ways for

finding the truth about a message .Here we

used a few machine learning algorithms to

classify them and detect them.

KEYWORDS:

Age,Gender,Machine learning

I. Introduction:

Nowadays,with the rapid spread in the use of

internet resources and the huge amount of

data shared on the internet,most of them were

almost untrustable ,and were not recognized

by people.Even a few People were afraid of

those messages ,even some messages are

leading to death of people.so as to detect

them this project is used to detect the peoples

who sent that message and classifies their age

and gender.So as we can control and stop

these kind of works.

II. LITERATURE SURVEY:

We already have several approaches to detect

gender and age through facial images.We

can do these classification on Gender Based

on Human faces has been detected.We have

collected certain datas of equipped work and

worked through it to detect age and gender

and mentioned the methods used below.Here

fig 1 indicates the Proposed age and gender

detection.

Fig. 1. The proposed age and gender

methodology

III.METHODOLOGY

In the section,we present the methodology of the

proposed age and gender detection system.The

first step is to input the data.The second step is

tokenization and extraction of the feature sets that

we will use later to build the classifier,where

tokenization of the data means chopping the text

into words,The third step is appling string to word

vector which is very important as it cleans the

data by removing unnecessary information in

order to improve system performance.The fourth

step is applying feature selection to the data.The

fifth step is applying the classifier using different

algorithm namely(random forest,naive

bayes,decision tree).The last step is producing the

output class and evaluating the results.

International Journal of Scientific Research in Engineering and Management (IJSREM)

Volume: 05 Issue: 10 | Oct - 2021 ISSN: 2582-3930

IV.PROPOSED SYSTEM

Here the algorithms we used are

RandomForestClassifier:

Random forests or random decision forests are

an ensemble learning method for classification

,regression and other tasks that operate by

constructing a multitude of decision trees at

training time.

Naive Bayes:

It is used to classify objects .It assumes strong,

independent attributes of data points.it also

includes spam filters, text analysis and even

medical diagnosis.

Decision tree classifier

A decision tree is a decision support tool that

uses a tree-like model of decision and their

possible

Consequences ,including chance event

outcomes, resource costs, and utility. It is one

way to display an algorithm that only contains

conditional control statements.

V.REQUIREMENTS:

● Python

● Jupyter notebook

DATA SETS:

The data is collected from project details ,By

classifying the data which satisfies all

requirements , the result is evolved

Fig2.dataset

fig3.datasets

VI.RESULTS:

The obtained acquired results are

fig.4. experimental data analysis for two

combined classifiers.

International Journal of Scientific Research in Engineering and Management (IJSREM)

Volume: 05 Issue: 10 | Oct - 2021 ISSN: 2582-3930

Fig.5 Age prediction

Fig.6 Age prediction with accuracy and

frequency.

Fig 7 Analysis of Face Score

Fig 8:Classification of data

Fig 9:Face detection

fig:10 Removing undetected option

International Journal of Scientific Research in Engineering and Management (IJSREM)

Volume: 05 Issue: 10 | Oct - 2021 ISSN: 2582-3930

fig:11 Final result

VII.CONCLUSION:

So we can Say that by using these algorithms to

certain data sets we can get the acquired results.

REFERENCES:

[1] Warkentin, Darcy, Michael Woodworth,

Jeffrey T. Hancock, and Nicole Cormier. (2010)

"Warrants and deception in computer mediated

communication." In Proceedings of the ACM

conference on Computer supported cooperative

work, pp. 9-12.

[2] Witten, Ian H., Eibe Frank, and A. Mark. "Hall,

and Christopher J Pal. 2016. Data Mining: Practical

machine learning tools and techniques."

AUTHOR’S PROFILE:

Mr. A.Lokesh, Student, B.E. Computer Science and

Engineering, Sri Chandrasekharendra SaraswathiViswa

Mahavidyalaya deemed to be university, Enathur,

Kanchipuram, India. Her Area of Big data analytics

Mr.Ch.Harsha Vardhan Reddy, Student, B.E. Computer

Science and Engineering, Sri Chandrasekharendr

saraswathi Viswa Mahavidyalaya deemed to be

university, Enathur, Kanchipuram, India. His Area of

Big data analytics

International Journal of Scientific Research in Engineering and Management (IJSREM)

Volume: 05 Issue: 10 | Oct - 2021 ISSN: 2582-3930

Mr. A.Sai kiran Student, B.E. Computer Science and

Engineering, Sri Chandrasekharendra SaraswathiViswa

Mahavidyalaya deemed to be university, Enathur,

Kanchipuram, India. Her Area of Big data analytics

Dr.C.K.Gomathy is Assistant Professor in

Computer Science and Engineering at Sri

Chandrasekharendra SaraswathiViswa

Mahavidyalaya deemed to be university,

Enathur, Kanchipuram, India. Her area of

interest is Software Engineering, Web Services,

Knowledge Management and IOT.

Review on Age and Gender Detection: Diverse approach and Algorithmic Insights

Conference Paper

Dec 2023

Warrants and deception in computer mediated communication

Conference Paper

Full-text available

Feb 2010

This article explores the operation of warrants, connections between online and real-world identities, on deceptive behavior in computer-mediated communication. A survey of 132 participants assessed three types of warrants (the use of a real name, a photo, and the presence of real-world acquaintances) in five different media: IM, Forums, Chat, Social Networking Sites (SNS) and Email. The effect of warrants on lies about demographic information (e.g., age, gender, education, etc.), one's interests (e.g., religion, music preferences, etc.), and the seriousness of lies was assessed. Overall, deception was observed most frequently in Chat and least often in SNS and Email. The relationship between warrants and deception was negative and linear, with warrants suppressing the frequency and seriousness of deception regardless of medium, although real-world acquaintances were especially powerful in constraining deception in SNS and emails. Author Keywords

Data Mining: Practical Machine Learning Tools and Techniques

Book

Nov 2016

Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches. Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research. Please visit the book companion website at http://www.cs.waikato.ac.nz/ml/weka/book.html It contains Powerpoint slides for Chapters 1-12. This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the book Online Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the book Table of contents, highlighting the many new sections in the 4th edition, along with reviews of the 1st edition, errata, etc. Provides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projects Presents concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods Includes a downloadable WEKA software toolkit, a comprehensive collection of machine learning algorithms for data mining tasks-in an easy-to-use interactive interface Includes open-access online courses that introduce practical applications of the material in the book.