SkInsight: AI-Powered Facial Analysis for Acne Detection

An advanced AI-driven platform for skin analysis and personalized skincare recommendations.
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Table of Contents

1. About The Project
2. Features and Objectives
3. Technologies Used
4. Getting Started
5. Future Scope & Challenges
6. Website Illustrations
7. Collaborators

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About The Project

SkinSight is an AI-powered dermatological platform designed to analyze facial images for acne detection, skin condition assessment, and personalized skincare recommendations. By leveraging deep learning models like YOLO, OpenCV, and TensorFlow, the platform provides accurate real-time analysis of skin health.

The project aims to bridge the gap between AI and dermatology by offering users a smart, accessible, and cost-effective solution for skincare management. SkinSight provides detailed skin analysis, customized product recommendations, dermatologist appointment booking, and progress tracking, ensuring a holistic skincare experience for users.

Built with Flask (Python) for the backend, React.js for the frontend, and SQLite3 for secure data storage, SkinSight is optimized for both scalability and user engagement. It empowers individuals with actionable insights and professional support, making advanced dermatological care more accessible than ever.

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Features and Objectives

• AI-Based Acne Detection
• Oiliness Level Assessment
• Personalized Skincare Recommendations
• Dermatologist Appointment Booking
• Progress Tracking for Skin Health
• Secure User Authentication and Data Encryption

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Technologies Used

• Frontend: React.js, Tailwind CSS
• Backend: Flask (Python)
• Database: SQLite3
• AI Models: YOLO, OpenCV, TensorFlow
• Deployment: Render

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Getting Started

Details

Prerequisites

• Python 3.8+
• Node.js & npm
• Virtual Environment (venv or conda)

Installation

1. Clone the Repository:

   git clone https://github.com/your-repo/SkinSight.git
   cd SkinSight

2. Setup Backend:

   cd backend
   pip install -r requirements.txt
   python app.py

3. Setup Frontend:

   cd frontend
   npm install
   npm start

4. Run the Project:
   • Access at http://localhost:3000

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Future Scope & Challenges

Future Scope

• Expanding detection to additional dermatological conditions.
• Integrating an AI-driven chatbot for skincare queries.
• Enhancing AI models for better accuracy and real-time processing.

Challenges Faced:

• Data diversity for unbiased AI predictions.
• Optimizing YOLO models for efficient performance.
• Simplifying UI for broader accessibility.

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Website Illustrations

Below are screenshots of key features of SkinSight:

• Login Page
  


• Signup Page
  



• Face Analysis Page
  


• New Appointment Page
  


• Doctor Appointment Page
  

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Collaborators

• Piyush Kumar (21BCE3077)
• Madhav Choudhary (21BCE3239)
• Anas Khan (21BCE3244)

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💙 If you like this project, give it a ⭐! In case of any bugs or feedback, feel free to contact me.