Gender-&-Age-Prediction 👦👧👩‍🦳🧔

This project uses OpenCV and deep learning models to detect faces in images and predict their age and gender.

Features ✨

• Face Detection: Detects faces in images using a pre-trained OpenCV DNN model.
• Age Prediction: Predicts the age range of the detected face using a Caffe-based deep learning model.
• Gender Prediction: Predicts the gender (Male or Female) of the detected face using a Caffe-based deep learning model.
• Image Processing: Processes each image in a specified folder and saves the output with detected faces, predicted age, and gender.

Technologies/Tools 🔧

• Python 3.x
• OpenCV (pip install opencv-python)
• numpy (pip install numpy)

Setup 🚀

1. Clone the repository:

   git clone https://github.com/LasithaAmarasinghe/Gender-and-age-prediction.git

2. Install dependencies:

   pip install -r requirements.txt

3. Download the required pre-trained models:

   • Face Detection Model: opencv_face_detector.pbtxt and opencv_face_detector_uint8.pb
   • Age Model: age_deploy.prototxt and age_net.caffemodel
   • Gender Model: gender_deploy.prototxt and gender_net.caffemodel

4. Prepare your image folder:

   • Create a folder named images and place the images you want to process in this folder.
   • Supported image formats: .png, .jpg, .jpeg. 🖼️

5. Create the output folder:

   • The processed images will be saved in the detected_images folder. This folder will be created automatically if it does not exist.

How This Works 🔍

1. Step 1: The script loads pre-trained deep learning models for face detection, age prediction, and gender prediction.
2. Step 2: It processes each image in the images folder:
   • Detects faces using the OpenCV DNN model.
   • For each detected face, it uses the age and gender models to predict the corresponding values.
   • Annotates the image with the predicted age and gender.
3. Step 3: The script saves the processed image in the detected_images folder.

Models Used 🏋️‍♂️

This project uses several pre-trained models to perform face detection, age prediction, and gender prediction.

1. Face Detection Model

• opencv_face_detector.pbtxt: This is the configuration file that defines the architecture of the face detection model. It specifies the layers, input sizes, and other necessary parameters to initialize the face detection network.
• opencv_face_detector_uint8.pb: This is the pre-trained model file containing the weights and parameters of the face detection network. It was trained on a large dataset and is capable of detecting faces in images.

2. Age Prediction Model

• age_deploy.prototxt: This is the configuration file that defines the architecture of the age prediction network. It includes details about the layers and the input structure.
• age_net.caffemodel: This is the pre-trained model file that contains the learned weights for the age prediction network. It was trained on a dataset that classifies ages into different ranges.

3. Gender Prediction Model

• gender_deploy.prototxt: This is the configuration file for the gender prediction network. It contains the model's architecture, including layer definitions and input configurations.
• gender_net.caffemodel: This is the pre-trained model file containing the learned weights for the gender prediction network. It classifies faces into two categories: Male and Female.

License 📝

This project is licensed under the MIT License. See the LICENSE file for details.