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Real-Time Gender Detection Using OpenCV and Keras

Posted by Hagos Shifare, last updated on
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This project highlights how to build a system that identifies gender from live video feeds.

Overview

Our goal is to create a real-time gender detection system that can analyze video frames to determine the gender of individuals captured by a webcam. We have trained a deep learning model (CNN) for gender classification with 90,000 custom created balanced face images and saved the model. The model has achieved 86% accuracy.

Tools and Libraries

  • OpenCV: A powerful library for computer vision tasks, including image processing and video capture.
  • Keras: A high-level neural networks API that we use to load our pre-trained gender classification model.
  • NumPy: A library for numerical operations on arrays.

Code Breakdown

The full code for this project is available on GitHub. You can clone the repository, run the script, and see the gender detection in action.

To fork the repository click the following github link:

ADMICSOL/Gender-Detection-Using-CNNs (github.com)

Here’s a step-by-step explanation of the code used to achieve this functionality:

  1. Import Libraries
  2. Load the Trained Model
  3. Define Gender Categories
  4. Load the Face Detector
  5. Initialize Video Capture
  6. Process Each Frame
    • Capture Frame: We read frames from the webcam and convert them to grayscale for face detection.
    • Detect Faces: We locate faces in the frame using the Haar cascade classifier.
    • Process Faces: For each detected face, we extract the region of interest (ROI), resize it, and normalize it for the model.
    • Predict Gender: We use the pre-trained model to predict the gender and display the result on the frame.

Conclusion

This real-time gender detection system is a practical example of how deep learning and computer vision can be combined to create useful applications. By leveraging pre-trained models and powerful libraries like OpenCV and Keras, we can build sophisticated systems with relative ease. Whether you’re interested in exploring more advanced features or integrating this into a larger project, the foundation provided here offers a solid starting point.

If you need a Gender detection model with 96% accuracy you can contact us here:

Thank you!.

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