Real-Time Gender Detection System Using OpenCV and Deep Learning

Audience-aware displays and smart-advertisement systems need a way to understand who is actually looking at them, in real time, using nothing more than a standard camera feed. Building that requires two things working together reliably: fast enough face detection to keep up with live video, and a classifier accurate enough to be useful for that.

I built a real-time gender detection pipeline in Python: OpenCV's Haar cascade classifier locates faces in each webcam frame, each detected face region is cropped and preprocessed, and a convolutional neural network trained on a labeled face dataset classifies it. The result is displayed as a live overlay on the video feed, frame by frame, so the classification updates continuously as the camera sees new faces.

• Python for the full pipeline
• OpenCV for real-time face detection and webcam frame capture
• Keras / a convolutional neural network for the gender classification model
• NumPy for image array preprocessing

• Real-time inference directly from a live webcam feed, not just static images
• A dedicated training script (train.py) so the classification model can be retrained on a different labeled face dataset
• A separate, versioned model file (gender_detection.model) decoupled from the inference script, so the model can be swapped without touching the detection code

I designed and built the entire pipeline solo: the face-detection preprocessing step, the CNN architecture and training script, and the real-time webcam inference loop that ties them together.

The full source, including the training script and the dataset structure, is available on GitHub:

View source on GitHub