Gender Recognition using Local Binary Pattern and Naïve Bayes Classifier
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Date
2016-10-24
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African Journal Online. Published by Nigeria Computer Society Journal. Journal of Computer Science and its Applications
Abstract
Human 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.
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Citation
Babatunde et. al., 2016