Gender Detection : Classification Face male/female using multi databases

Language: English

Published by LAP LAMBERT Academic Publishing, 2021

6204182811 / 9786204182810

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Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH

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nach der Bestellung gedruckt Neuware - Printed after ordering - Today's machine learning is widely used in diverse areas. For example, fraudulent systems, recommended systems, exploited prediction, and many other applications. One of these applications, is being exploited in this search. This paper presents an approach to detecting a person's gender through the front face image by using extraction features and classification techniques. Gender prediction can be a very useful method in HCI (Human Computer Interaction) systems. As a very powerful method of extracting data, the classification is used here to collect class data and to classify the gender as either male or female. To extract data features, Local Binary Pattern (LBP) is used, whereas the Random Forest (RF) algorithm of classification is used to gauge the maximum accuracy. Various database models were used in this search: ORL database, FEI database, Jaffe database, and CUHK database where JAFFE database gave a very high level of accuracy which is 99.89% in contrast CUHK database which gave a lower level of accuracy 76.18% with relative stability. Details of the prediction model and results model are reported in this paper.

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Title
Gender Detection : Classification Face male/female using multi databases
Author
Ezz Omar
Publisher
LAP LAMBERT Academic Publishing
Publication year
2021
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
6204182811
ISBN 13
9786204182810
Item weight
137 grams
Dimensions
220x150x6 mm

AHA-BUCH GmbH

Einbeck, Germany

5-star seller

AbeBooks seller since August 14, 2006

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