Gender Detection Model using Keras

The Gender Detection (From Scratch) project utilizes deep learning techniques to develop a model capable of accurately predicting the gender of individuals based on facial images. The model is trained on a dataset of approximately 2,200 face images using the Keras framework and cvlib library. It achieves a training accuracy of around 96% and a validation accuracy of approximately 90% on a separate validation dataset. The saved model can be used for gender prediction on new images using Flask.

Category:

Sub-category:

Deep Learning

Computer Vision

Overview:

This project aims to develop a gender detection model using deep learning techniques with the Keras framework and the cvlib library. The model is trained from scratch on a dataset of approximately 2,200 face images, where the face regions are cropped using cvlib's face detection functionality. The SmallerVGGNet architecture is employed for training, achieving a training accuracy of around 96% and a validation accuracy of approximately 90% on a 20% validation dataset.

Description:

The Gender Detection (From Scratch) project utilizes deep learning to create a model capable of accurately predicting the gender of individuals based on facial images. The training dataset consists of around 2,200 face images, evenly distributed between male and female classes. These face images are obtained by collecting data from Google Images and then using cvlib's face detection algorithm to crop the facial regions.

To evaluate the model's performance, a separate validation dataset is created, comprising 20% of the original dataset. The model's accuracy is measured on this validation dataset, providing insights into its ability to generalize to unseen data. The achieved results demonstrate a training accuracy of approximately 96% and a validation accuracy of around 90%, highlighting the model's robustness in predicting gender from facial images. The saved model can then be used to predict the gender of new images using Flask.

The gender detection model developed in this project has various practical applications, including facial recognition systems, demographic analysis, and targeted advertising. By accurately identifying gender from facial images, the model can enhance security measures, personalize user experiences, and support gender-based research. The combination of the Python programming language, Keras framework, and cvlib library provides a powerful and efficient solution for developing and deploying deep learning models for gender detection tasks.