Armeen 02 06 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 humancomputer 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 selforganizing neural networks to obtain a suitable categorization these networks extract weights for each gender with minimal error The outcomes are contrasted with preexisting data sets in order to demonstrate the superiority of the suggested approach Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026