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ESP32-CAM Face Recognition
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ESP32
ESP32BeginnerAdvancedesp32-camface-recognitionedge-ai

ESP32-CAM Face Recognition

Build an ESP32-CAM based face recognition system that identifies known faces in real time using on-device AI.

₹2728.00

Inclusive of all taxes

Development fee₹1532.00
Components (from library)₹1196.00
Total per unit (incl. GST)₹2728.00
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Total: ₹2728.00

₹2728.00

Inclusive of all taxes

Components

6 items

Difficulty

Advanced

Build Time

3 – 5 hours (training + testing)

Project Details

The ESP32-CAM Face Recognition project extends face detection by identifying who the person is, not just detecting a face. Using optimized edge-AI models from Espressif (ESP-WHO framework), the ESP32-CAM detects a face, extracts facial features, compares them with stored profiles, and recognizes registered individuals in real time. All processing happens locally on the ESP32-CAM, ensuring privacy and low latency. This project is commonly used in smart door locks, attendance systems, access control, smart home security, and research & AI demos.

How It Works

The ESP32-CAM uses the ESP-WHO AI framework to perform face detection and recognition. First, it detects faces in the camera feed. Then, it extracts facial features (landmarks, embeddings) from the detected face. These features are compared against stored profiles of registered individuals. If a match is found above a certain threshold, the system identifies the person. All processing happens locally on the ESP32-CAM's processor, ensuring real-time performance and privacy. The system can be accessed via a web browser to enroll new faces and view recognition results.

Prerequisites

Basic knowledge of Arduino programming, familiarity with ESP32 boards, understanding of basic electronics and wiring, and experience with web interfaces. Users should also understand the concepts of AI and computer vision at a beginner level.

What You'll Learn

Difference between detection and recognition

Understanding how face detection differs from face recognition in computer vision

Feature extraction in computer vision

Learning how facial features are extracted and compared for identification

On-device AI constraints

Understanding limitations and optimizations for edge AI processing

Real-time AI optimization

Techniques for achieving real-time performance on embedded systems

Embedded security system design

Designing privacy-focused security systems using edge AI

Key Features

Real-time face recognition

Identifies registered individuals in real time using on-device processing

On-device AI (no cloud)

All processing happens locally on the ESP32-CAM, ensuring privacy and low latency

Face enrollment via browser

Users can enroll faces through a web interface

Privacy-focused design

No data sent to cloud, keeping face data local

Expandable to access control

Can be integrated with door locks or access systems

#esp32-cam#face-recognition#edge-ai#computer-vision#iot-security#ai-camera

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