Gender Prediction through Image Analysis: A CNN Model Approach

Deep learning finds use in many fields, including security, marketing, and healthcare; gender classification has drawn a lot of interest in all of them. This paper uses an open-source Kaggle dataset to show a convolutional neural network model for gender classification. There are 2250 pictures in all, equally divided between males and women. Image preprocessing to maintain a constant size of 126x126 pixels allowed them be utilized into the model. The CNN architecture consists in three convolutional layers plus max-pooling layers, dropout layers for regularity, and fully connected layers for classification. The model was trained using eighty percent of the data; twenty percent was used for validation. With balanced performance across both classes, the model obtains an accuracy of 79%, per results. Though validation loss varies somewhat, the model shows good gender classification, which makes it an interesting tool for practical uses. By tuning the model for improved generalization and performance, one can make still more progress.