HUMAN GENDER DETECTION SYSTEM BASED ON FACIAL IMAGE USING CONVOLUTIONAL NEURAL NETWORK ALGORITHM

Abstract

The demand for system automation has been continuously increasing with the current technological developments. One of these advancements is in the implementation of face recognition. Camera capabilities have evolved from merely capturing images or videos to being able to process the resulting images. Facial images contain a wealth of information, one of which is the gender information of the individuals. To obtain this information, facial image classification using deep learning is required. In this scientific paper, the author utilizes the Convolutional Neural Network algorithm implemented with the Python programming language and employs TensorFlow as its framework. The research aims to predict human gender based on facial images. The dataset used in this study is obtained from the kaggle.com dataset provider, consisting of 9,600 male facial data and 9,600 female facial data. The data is divided into a training and testing set, with an 80% ratio for training data and a 20% ratio for testing data from the total available data. The model training process is performed for 15 epochs with 768 steps in each epoch. The testing results show that the Convolutional Neural Network method achieves a validation accuracy of approximately 91%. The developed program runs well through a webcam.