Gender Recognition using Local Binary Pattern and Naïve Bayes Classifier

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dc.contributor.authorBabatunde, R. S., Abdulsalam, S. O., Yusuff, S. R., and Babatunde, A. N. (2016):
dc.date.accessioned2024-07-24T09:31:53Z
dc.date.available2024-07-24T09:31:53Z
dc.date.issued2016-10-24
dc.description.abstractHuman face provides important visual information for gender perception. Ability to recognize a particular gender is very important for the purpose of differentiation. Automatic gender classification has many important applications, for example, intelligent user interface, surveillance, identity authentication, access control and human-computer interaction amongst others. Gender recognition is a fundamental task for human beings, as many social functions critically depend on the correct gender perception. Consequently, real-world applications require gender classification on real-life faces, which is much more challenging due to significant appearance variations in unconstrained scenarios. In this study, Local Binary Pattern is used to detect the occurrence of a face in a given image by reading the texture change within the regions of the image, while Naive Bayes Classifier was used for the gender classification. From the results obtained, the gender correlation was 100% and the highest accuracy of the result obtained was 99%.The system can be employed for use in scenarios where real time gender recognition is required.
dc.identifier.citationBabatunde et. al., 2016
dc.identifier.urihttps://www.ajol.info/index.php/jcsia/article/view/153913
dc.identifier.urihttps://kwasuspace.kwasu.edu.ng/handle/123456789/1772
dc.language.isoen
dc.publisherAfrican Journal Online. Published by Nigeria Computer Society Journal. Journal of Computer Science and its Applications
dc.titleGender Recognition using Local Binary Pattern and Naïve Bayes Classifier
dc.typeArticle
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