Ai Face Shape Detection Project Python with Source Code

This project involves a Flask-based web application for real-time face detection and face shape. It leverages MediaPipe for facial landmark detection and Random Forest for face shape classification. Users can upload images to get annotated results or view real-time face detection through their webcam.
Features :-
Real-Time Face Detection
: Detect and annotate faces in real-time using your webcam.
Image Upload and Annotation
: Upload an image to get it processed and annotated with detected facial features.
Face Shape Classification
: Classify face shapes based on detected landmarks using a pre-trained model.
Data and Model Information :-
Face Shape Classification Model
Model File:
: Best_RandomForest.pkl
Description:
: This model is trained using Random Forest on a dataset of facial landmarks. It classifies face shapes into categories such as Heart, Oval, Round, and Square.
Training Data
: The model was trained on a dataset of labeled face shapes with corresponding landmark features extracted using MediaPipe.
Face Landmarker Model
Model File:
: face_landmarker_v2_with_blendshapes.task
Description:
: This MediaPipe model detects facial landmarks and provides blendshapes for facial expressions.
Training Data
: Used to detect key facial landmarks required for both face shape classification and real-time annotations.
Technology Used in the project :-
We have developed this project using the below technology
HTML : Page layout has been designed in HTML
CSS : CSS has been used for all the desigining part
JavaScript : All the validation task and animations has been developed by JavaScript
Python : All the business logic has been implemented in Python
Flask: Project has been developed over the Flask Framework
Supported Operating System :-
We can configure this project on following operating system.
Windows : This project can easily be configured on windows operating system. For running this project on Windows system, you will have to install
Python 3.8, PIP, Django.
Linux : We can run this project also on all versions of Linux operating systemMac : We can also easily configured this project on Mac operating system.
Installation Step : -
python 3.8
command 1 -
python -m pip install --user -r requirements.txt
command 2 -
python app.py
Demo Video