Age and Gender Prediction

This project is an age and gender prediction tool that uses OpenCV's Haar Cascade Classifier for face detection and a pre-trained model to estimate age and gender based on detected faces. Currently, the project leverages Caffe-based deep learning models for age and gender classification, and OpenCV for image processing and face detection.

Features

• Face Detection: Uses OpenCV's Haar Cascade Classifier to detect faces in an image.
• Age Prediction: Predicts the age group of each detected face using a pre-trained age prediction model.
• Gender Prediction: Determines the gender (male or female) of each detected face.
• Confidence Filtering: Only displays predictions for age and gender if the model's confidengit is above a certain threshold to improve reliability.

Future Improvement

Advanced deep learning models will be used in future updates for improving age and gender predictions. These advanced models have to be fine-tuned on a larger and more diversified dataset to provide higher accuracy in predicting age across different ranges and demographic groups. This approach will involve training or transferring learning on a new deep learning model that can handle complex variations in lighting, angle, and facial expressions.

Structure

AgeGender.py: Main script for face detection, age, and gender prediction. age_net.caffemodel, gender_net.caffemodel: Pre-trained models for age and gender classification. age_deploy.prototxt, gender_deploy.prototxt: Model configuration files for Caffe. sample1.jpg: Example input image. README.md: Project documentation.

Dependencies

Python 3.x OpenCV Numpy Caffe (for deep learning model compatibility with OpenCV's DNN module)