Gender classification is a fundamental task in computer vision and machine learning with applications ranging from targeted marketing and user profiling to human-computer interaction and security systems. With the increasing availability of image data and the advancements in deep learning, automatic gender recognition from facial images has become both feasible and efficient.
This project presents a gender classification system built using TensorFlow, a powerful open-source deep learning framework developed by Google. The goal is to train a model that can accurately classify a person’s gender based on facial features extracted from images. Leveraging convolutional neural networks (CNNs), which are particularly effective for image-based tasks, the system learns to detect and interpret patterns associated with male and female facial characteristics.
The project involves several key steps, including dataset preprocessing, model design, training, evaluation, and testing. TensorFlow's flexibility and robust ecosystem make it an ideal choice for experimenting with different architectures and fine-tuning performance. Through this project, we aim to explore how modern deep learning techniques can be applied to gender classification tasks and evaluate the effectiveness of such approaches in real-world scenarios.
Dataset: CelebFaces Attributes (CelebA) Dataset: https://www.kaggle.com/datasets/jessicali9530/celeba-dataset