PersonalColorAnalyst

Project Overview

The goal is to develop an AI-powered personal color analysis tool that helps users determine the best color palette for their skin tone, hair, and eyes. This will involve multiple AI models working together to analyze facial features and suggest optimal color choices for clothing and accessories.

Key Components

Image Processing & Segmentation

1- Develop a computer vision model to segment and detect key facial features (ongoing)

• Skin color detection (face and neck area).
• Eye color detection.
• Hair color detection. Use deep learning (e.g., CNNs, GANs)

2- Face Shape Detection

• Train a model to classify face shapes (oval, round, square, heart, etc.).
• Use key landmark detection techniques to analyze facial contours.

3- Skin Tone Classification (ongoing)

• Implement an algorithm to match skin tone with predefined categories (cool, warm, neutral).
• Consider using color theory and databases like the Fitzpatrick scale for classification.
• Leverage machine learning to refine tone matching based on dataset training.

4- Color Palette Recommendation

• Based on the detected skin, hair, and eye colors, suggest the best color palettes for clothing and accessories.
• Use color theory principles (seasonal color analysis: Spring, Summer, Autumn, Winter) to generate recommendations.
• Implement an AI model to personalize suggestions based on user preferences.

5- AI Model Integration

• Combine multiple AI models into a unified system.
• Use an API or microservices architecture to allow smooth communication between models.
• Optimize processing speed to ensure real-time or near-real-time results.

Tools

• Computer Vision & Image Processing: OpenCV, TensorFlow, PyTorch
• Deep Learning Models: CNNs for feature detection, GANs for color enhancement
• Machine Learning: Scikit-learn for classification, custom-trained models for color matching
• Backend & API Integration: Python (FastAPI, Flask), cloud services for AI inference

Main Steps of the project

• Dataset Collection: Gather images for training and validation.
• Model Training: Develop separate models for segmentation, shape detection, and color matching.
• Integration & Testing: Combine models and fine-tune performance.
• User Interface Development: Create a user-friendly experience.
• Deployment: Deploy as a web or mobile application with cloud-based AI processing.