Gender identification software is becoming increasingly popular in the age of machine-to-machine interactions, as the need for person-aligned, effective, and ethical systems becomes more apparent. In this age of machine-to-machine interactions, automated gender identification software is becoming more popular. The bulk of the systems for gender identification that were examined used either textual or audiovisual data as input to determine their gender. Many different approaches to automatically detect gender based on traits gathered from people's bodies and/or behaviours have been suggested, and there are simply too many to include here. The accuracy of automated gender identification, on the other hand, has always been a topic of worry or a source of criticism. Initial face identification is accomplished by the use of the Haar Cascade face detection method, which is based on the Viola-Jones face recognition algorithm. Facial characteristics such as the eyes, mouth, and nose are then identified. Second, using the Viola-Jones face detection algorithm, the faces and facial characteristics such as the eyes, mouth, and nose are recognised, and the results are shown. In order to increase the accuracy of gender identification, adaptive filters are employed to reduce the amount of noise present before the detection process is carried out. The facial qualities that have been gathered are utilised as input or test data for the neural network, which is subsequently trained on these characteristics. It is intended that the neural network acquire the features of the genders and then function as a classifier when it comes to detecting them. Instead of saying it another way, a neural network known as Keras is used for feature extraction and gender identification, which is free and open-source software