Age & Gender Recognition using OpenCV DNN

This project uses OpenCV's deep learning module (DNN) to detect faces in video frames and predict the age and gender of individuals in real-time. The model utilizes pre-trained convolutional neural networks for accurate age and gender classification.

Introduction

The Age & Gender Recognition system is designed to detect faces and predict the age range and gender of individuals from video input. This project makes use of pre-trained models for face detection, age classification, and gender classification, enabling real-time predictions.

Features

1. Real-time face detection in video.
2. Age classification into predefined age groups.
3. Gender classification (Male/Female).
4. Supports both real-time video capture and video file input.

Requirements

1. Python 3.x

2. OpenCV

3. Pre-trained models for:

   -Face detection

   -Age prediction

   -Gender prediction

Installation

1. Clone the repository:

    git clone https://github.com/sanskarsri26/Age_gender_recognize.git
   
    cd Age_gender_recognize
   

2. Install dependencies:

Make sure you have OpenCV installed. You can install OpenCV using pip:

pip install opencv-python

3. Download the pre-trained models:

Download the following model files and place them in your project directory:

--opencv_face_detector.pbtxt and opencv_face_detector_uint8.pb (for face detection)

--age_deploy.prototxt and age_net.caffemodel (for age prediction)

--gender_deploy.prototxt and gender_net.caffemodel (for gender prediction)

** Usage**

1. Running the Project

   Run the Python script:

This script processes video input, detects faces, and predicts the age and gender of individuals.

python age_gender_recognition.py

2. Input Video:

The script is set to read from a video file (4.mp4 in this case). You can change this to use your webcam or another video source.

To use the webcam, replace:

video = cv2.VideoCapture('4.mp4')

with:

video = cv2.VideoCapture(0)

Exiting:

Press q to quit the video processing window.

Explanation

Face Detection: Detects faces in each video frame using a pre-trained model.

Age Prediction: Predicts the age group of the detected faces based on the age_net.caffemodel.

Gender Prediction: Classifies gender as Male or Female using the gender_net.caffemodel.

The predicted age and gender are displayed as text labels over each detected face in the video feed.

Models

Ensure the following models are available in the root directory:

Face Detection:

opencv_face_detector.pbtxt

opencv_face_detector_uint8.pb

Age Detection:

age_deploy.prototxt

age_net.caffemodel

Gender Detection:

gender_deploy.prototxt

gender_net.caffemodel

The project uses OpenCV's DNN module to load these models and make predictions.