https://github.com/karan071/face-application
This project implements a Face Detection app with a FastAPI backend, using DeepFace for recognition, Milvus DB for face embedding storage, and Prisma ORM for database management. The frontend is built with ReactJS, offering an intuitive interface for face detection tasks.
https://github.com/karan071/face-application
docker dockercompose fastapi javascript jsx milvus prisma python reactjs
Last synced: 3 months ago
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This project implements a Face Detection app with a FastAPI backend, using DeepFace for recognition, Milvus DB for face embedding storage, and Prisma ORM for database management. The frontend is built with ReactJS, offering an intuitive interface for face detection tasks.
- Host: GitHub
- URL: https://github.com/karan071/face-application
- Owner: Karan071
- Created: 2025-01-03T08:01:11.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-01-22T05:32:38.000Z (9 months ago)
- Last Synced: 2025-03-05T06:33:56.199Z (7 months ago)
- Topics: docker, dockercompose, fastapi, javascript, jsx, milvus, prisma, python, reactjs
- Language: Jupyter Notebook
- Homepage:
- Size: 16.7 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Face Recognition Backend API & ReactJs for Front-End
## Overview
This project provides a backend for a face recognition application, using **FastAPI**, **Prisma ORM** for PostgreSQL, and **Milvus** for vector database storage and similarity search. **DeepFace** is used for facial recognition and embedding extraction.
## Features
- Employee registration with face embedding storage.
- Visitor registration with face embedding storage.
- Face recognition for employees and visitors.
- Integration with Prisma (PostgreSQL) for structured data storage.
- Integration with Milvus for vector similarity search.
- Preloading of DeepFace model for optimized performance.
## Installation
### Prerequisites
- Python 3.9+
- PostgreSQL
- Milvus
- Node.js (for Prisma migrations)
### Environment Variables
Create a `.env` file with the following variables:
```
DATABASE_URL=postgresql://user:password@localhost:5432/database_name
MILVUS_HOST=localhost
MILVUS_PORT=19530
```
### Dependencies
Install the required Python packages:
```bash
pip install fastapi uvicorn prisma pymilvus deepface Pillow numpy python-dotenv pytz
```
## API Endpoints
### 1. Root Endpoint
**GET /**
**Description:** Test endpoint to verify the server is running.
**Response:**
```json
{"message": "Face App Backend"}
```
### 2. Employee Registration
**POST /register-employee/**
**Description:** Register a new employee with personal details and face embedding.
**Form Data:**
- `name`: Employee name (string)
- `age`: Employee age (integer)
- `gender`: Employee gender (string)
- `photo`: Employee photo (file)
- `designation`: Job designation (string)
- `contactNumber`: Contact number (string)
- `department`: Department (string)
- `description`: Additional description (string)
**Response:**
```json
{"message": "Employee registered successfully."}
```
### 3. Visitor Registration
**POST /register-visitor/**
**Description:** Register a visitor with personal details and face embedding.
**Form Data:**
- `name`: Visitor name (string)
- `age`: Visitor age (integer)
- `gender`: Visitor gender (string)
- `photo`: Visitor photo (file)
- `contact`: Contact number (string)
- `purposeOfVisit`: Purpose of the visit (string)
- `description`: Additional description (string)
**Response:**
```json
{"message": "Visitor registered successfully."}
```
### 4. Recognize Employee
**POST /recognize-employee/**
**Description:** Recognize an employee from a photo.
**Form Data:**
- `photo`: Uploaded photo (file)
**Response:**
If a match is found:
```json
{"name": "John Doe", "similarity": 0.85, "status": "success"}
```
If no match is found:
```json
{"message": "No match found", "status": "failed"}
```
### 5. Recognize Visitor
**POST /recognize-visitor/**
**Description:** Recognize a visitor from a photo.
**Form Data:**
- `photo`: Uploaded photo (file)
**Response:**
If a match is found:
```json
{"name": "Jane Doe", "similarity": 0.90, "status": "success"}
```
If no match is found:
```json
{"message": "No match found", "status": "failed"}
```
## Backend Architecture
### 1. FastAPI
- Framework for building the backend.
- Provides routing, middleware, and dependency injection.
### 2. Prisma ORM
- Manages structured data in PostgreSQL.
- Used to store:
- Employee details
- Visitor details
### 3. Milvus
- Vector database for storing face embeddings.
- Used for similarity searches.
### 4. DeepFace
- Extracts facial embeddings.
- **Model used:** VGG-Face.
## Database Structure
### PostgreSQL Tables
#### Employees Table
| Column | Type | Description |
|----------------|--------|----------------------|
| `id` | UUID | Primary key |
| `name` | String | Employee name |
| `age` | Integer| Employee age |
| `gender` | String | Employee gender |
| `photoBase64` | Text | Base64-encoded photo |
| `designation` | String | Job designation |
| `contactNumber`| String | Contact number |
| `department` | String | Department |
| `description` | String | Additional description|
#### Visitors Table
| Column | Type | Description |
|-----------------|--------|----------------------|
| `id` | UUID | Primary key |
| `name` | String | Visitor name |
| `age` | Integer| Visitor age |
| `gender` | String | Visitor gender |
| `photoBase64` | Text | Base64-encoded photo |
| `contact` | String | Contact number |
| `purposeOfVisit`| String | Purpose of the visit |
| `description` | String | Additional description|
## Key Functions
1. **`extract_face_embedding(photo_bytes: bytes)`**
- Extracts face embedding from photo bytes using DeepFace.
- Returns a 4096-dimensional embedding.
2. **`save_to_milvus(collection_name, name, embedding)`**
- Saves normalized embeddings to Milvus.
3. **`search_in_milvus(collection_name, query_embedding, threshold=0.6)`**
- Searches Milvus for a match to the query embedding.
- Returns the best match if similarity exceeds the threshold.
4. **`save_to_prisma(name, age, gender, photo_base64, table, additional_data)`**
- Saves user details to PostgreSQL using Prisma ORM.
## Running the Application
### Run Locally
```bash
uvicorn main:app --reload
```
### Access the API
- **Swagger UI:** [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs)
## Future Enhancements
- Implement rate limiting for API endpoints.
- Add authentication and authorization.
- Use GPU acceleration for faster embedding extraction.
- Integrate more facial recognition models.
# Screenshots
## Backend : FastAPI
## Milvus DB : using Attu for UI
## Primsa - Using Postgres (ORM with Prisma Python Client)
# FrontEnd using React JS
## Landing Page
## Main Dashboard
## Check In interface : Dashboard For checkIn (Visitor / Employee) : Face recognition
## Custom Error-Page for handling unauthorized requests
## Face recognition Working
## Self Check-In model for visitor/employee
Employee
Visitor
Working Model Representation
# Scope for Improvements
1) Rate limiter for requests
2) Authentication
3) Session using redux and redux tools (setting up store and auth-sessions)
4) Form validations
5) Pydantics models for Proper models