Article
Application of Binary Features and Gabor Filters for Gender Recognition
One significant biometric is the human face. contains a wealth of helpful data, including identity, gender, age, and race. Classifying people by gender is extremely easy for humans, but it can be difficult for computers. Gender classification from facial photos has garnered a lot of attention lately. When it comes to human-computer interface, gender detection can be helpful for things like individual designation. Our suggested approach is based on Gabor filters and Local Binary Patterns (LBP), which extract face features that are robust against interference. A number of algorithms have been developed for this aim, and the proportion of each of these problems has been resolved. We employed self-organizing neural networks to obtain a suitable categorization; these networks extract weights for each gender with minimal error. The outcomes are contrasted with pre-existing data sets in order to demonstrate the superiority of the suggested approach.
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