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AI-Powered 2D Hair Analysis Using Single View Image

AI-Powered 2D Hair Analysis Using Single View Image
Category:
AI / FMCG Beauty Care

The Problem Statement

In the personal care and cosmetics industry, understanding hair characteristics is crucial for developing better hair care products and offering personalized recommendations. Traditional hair analysis methods rely on physical sampling, microscopic imaging, and expert evaluation, which are time-consuming, expensive, and subjective. The challenge is to develop an AI-powered solution that can analyze hair quality, structure, and orientation from a single-view image, providing insights into fizziness, combing patterns, and 3D hair reconstruction without the need for specialized imaging equipment. 

The Solution

To address this challenge, we designed a computer vision and deep learning pipeline capable of performing a complete hair analysis from a single-view image. The pipeline involves multiple AI models trained for: 

  1. Bulk Hair Segmentation – Identifying the overall hair region. 
  2. Alpha Matte Generation – Refining segmentation to isolate fine hair strands at the boundary. 
  3. Strand and Depth Map Estimation – Extracting individual hair strands and their depth information. 
  4. 3D Hair Reconstruction – Generating a 3D hair strand model from a single image using strand and depth maps. 

This AI-driven solution automates and optimizes hair analysis, enabling rapid and accurate insights without requiring high-end imaging hardware. This AI-powered hair analysis system leverages computer vision and deep learning to provide detailed hair insights from a single image. The solution is fast, scalable, and cost-effective, making it valuable for the cosmetic, dermatology, and e-commerce industries. By enabling 3D hair reconstruction, frizz analysis, and orientation detection, this technology paves the way for next-generation hair diagnostics and virtual hair modeling. 

Features of the Solution:

  • Bulk Hair Segmentation 
    1. Trained a deep learning model on diverse hair datasets to segment the hair region in an image. 
    2. Used data augmentation techniques to handle variations in hair color, background, lighting, and texture. 
  • Alpha Matte Model for Fine Grained Hair Segmentation 
    1. To capture finer hair strands, we trained an Alpha Matte model using a dataset with manually labelled hair mattes. 
    2. Developed an AI model for automatic generation of a Tri-map for any given hair image. 
    3. Used a combination of Tri-map generation and matting networks to generate a high-quality alpha matte, preserving even the thinnest strands of hair. 
  • Strand and Depth Map Estimation 
    1. Trained separate deep learning models to extract hair strands orientation map and depth map 
    2. Manually annotated each hair strand and trained an AI Hair strand map model 
    3. Given a single view image, model generates a strand map and a depth map as outputs, which are intermediate representations for modelling 3D hair strands using a single view. 
  • 3D Hair Reconstruction from Single-View Image 
    1. Combined strand maps and depth maps to generate a 3D hair model using a differentiable rendering approach. 
    2. In the output, we get a realistic 3D hair reconstruction, enabling visualization from different angles. 
  • Fizziness Score Calculation 
    1. Fourier Transform and Edge Detection techniques to identify frizzy hair regions. 
    2. Developed a custom fizziness score computation for analyzing flying hair strands and hairstyle. 
    3. Fizziness score (0-10 scale) given as follows 
      • 0 (Smooth Hair) – No stray strands. 
      • 1-3 (Low Frizz) – Few deviations. 
      • 4-7 (Medium Frizz) – Noticeable strand irregularities. 
      • 8-10 (High Frizz) – Extreme flyaway and disorder. 

 

Features of the hair analysis Solution

Visualization:

  • Overlay segmented hair mask on the input image for verification. 
  • Display alpha matte output showing individual hair strands. 
  • Generate heatmaps for fizziness levels and strand orientation. 
  • Provide interactive 3D visualization of the reconstructed hair model, allowing users to rotate and zoom. 

Optimization & Porting

Used TensorRT and ONNX optimization to make models lightweight and fast for real-time execution. Leveraged GPU acceleration to process hair segmentation, strand map, and depth estimation in parallel. Dynamically adjusted image resolution to balance accuracy and speed. Created an offline version using PyTorch for professional use. Integrated with cloud-based services (AWS, Google Cloud) for scalable processing. 

Benefits Delivered

  • Automated Hair Analysis with High Precision 
  • Helps in recommending hair care routines based on individual hair texture, fizziness, and orientation. 
  • Reduces dependence on expensive microscopic analysis. 
  • Cosmetic Industry: Hair product companies can analyse hair types for customized formulations. 
  • Dermatology & Healthcare: Dermatologists can diagnose hair-related conditions remotely. 
  • Virtual Try-On & AR Applications: Enables realistic hair simulation for virtual hair styling.