Defining the perceptual threshold for hair volume: A method for aligning image-based quantification with sensory...

Area of Science:

• Cosmetic Science
• Perception Science
• Image Analysis

Background:

• Instrumental hair volume measurements often fail to correlate with consumer perception.
• A disconnect exists between objective in-vitro data and subjective sensory evaluation of hair volume.
• Understanding the Just-Noticeable Difference (JND) is crucial for product development and claims.

Purpose of the Study:

• To determine the JND for hair volume.
• To develop a predictive model for perceived hair volume that incorporates hair morphology, specifically waviness.
• To bridge the gap between instrumental measurements and sensory perception in hair volume assessment.

Main Methods:

• Quantified hair bulk volume and waviness using a custom image-analysis framework.
• Conducted sensory evaluation with an expert panel using controlled hair swatch sets.
• Developed and validated a multivariable regression model using Ordinary Least Squares and Leave-One-Group-Out Cross-Validation.

Main Results:

• Identified the JND for hair volume at approximately 3 cm².
• Bulk volume was the primary predictor (β=0.798), with waviness as a significant secondary factor (β=0.135).
• The validated model demonstrated robust predictive performance, achieving 66.67% top-rank accuracy.

Conclusions:

• Established a quantitative perceptual threshold for hair volume.
• Introduced a morphology-aware correction model that improves alignment between instrumental and sensory data.
• Provided a practical framework for validating volumizing product claims based on both physical measurement and perceptual relevance.