https://github.com/gbennnn/facial-landmarks-detection
Detects a person’s age range, gender, and facial landmarks in real time via webcam or from uploaded images.
https://github.com/gbennnn/facial-landmarks-detection
deep-learning flask landmark-detection machine-learning opencv python
Last synced: 4 months ago
JSON representation
Detects a person’s age range, gender, and facial landmarks in real time via webcam or from uploaded images.
- Host: GitHub
- URL: https://github.com/gbennnn/facial-landmarks-detection
- Owner: gbennnn
- License: mit
- Created: 2025-08-16T23:29:49.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-08-16T23:42:52.000Z (10 months ago)
- Last Synced: 2025-08-17T01:11:17.224Z (10 months ago)
- Topics: deep-learning, flask, landmark-detection, machine-learning, opencv, python
- Language: Python
- Homepage:
- Size: 92.5 MB
- Stars: 4
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.MD
- License: LICENSE
Awesome Lists containing this project
README
# Age, Gender & Facial Landmark Detection Web App
A web-based application built with **Flask**, **OpenCV**, and **MediaPipe** to detect **age**, **gender**, and **facial landmarks** from images or live camera feed.
## Features
- **Realtime Camera Detection**
- Uses your webcam directly in the browser.
- Performs live detection of **age**, **gender**, and **facial landmarks**.
- **Image Upload Detection**
- Upload an image from your device.
- The app processes the image and returns the detection result.
- **Modern UI**
- Styled with [Tailwind CSS](https://tailwindcss.com/) via CDN.
- Responsive and minimal interface.
## Preview
## Tech Stack
- **Backend**: Python, Flask
- **Computer Vision**: OpenCV, MediaPipe
- **ML Models**: Pre-trained Age & Gender detection models
- **Frontend**: HTML, Tailwind CSS
## Installation
### 1. Clone Repository
```bash
git clone https://github.com/gbennnn/facial-landmarks-detection.git
cd facial-landmarks-detection
```
### 2. Create Virtual Environment
```bash
python -m venv venv
source venv/bin/activate # Linux / Mac
venv\Scripts\activate # Windows
```
### 3. Install Dependencies
```bash
pip install -r requirements.txt
```
### 4. Prepare Models
[Download](https://github.com/spmallick/learnopencv/tree/master/AgeGender) the pre-trained models and place them in the `model/` folder:
* `deploy.prototxt`
* `res10_300x300_ssd_iter_140000.caffemodel`
* `age_deploy.prototxt`
* `age_net.caffemodel`
* `gender_deploy.prototxt`
* `gender_net.caffemodel`
## Usage
### Run the Flask App
```bash
python app.py
```
Open your browser and go to:
```
http://127.0.0.1:5000/
```
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.