International journal of cosmetic science · 2026
Defining the perceptual threshold for hair volume: A method for aligning image-based quantification with sensory evaluation.
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Muthunilavan N, Sridhar Rajam R, Gurulakshmi G
Аннотация
OBJECTIVE: Instrumental methods for measuring hair volume can detect physical differences that are not perceptible to consumers, creating a disconnect between in-vitro measurements and real-world perception. This study aimed to establish the Just-Noticeable Difference (JND) for hair volume and to develop a predictive model incorporating hair waviness to better align instrumental data with sensory evaluation.
METHODS: Bulk volume and waviness were quantified from hair swatches using a custom image-analysis framework. Volume was determined via pixel intensity-based fibre density classification, while waviness was derived from an amplitude-frequency analysis of the detrended medial axis. Sensory evaluation was conducted by an expert panel across controlled four-swatch sets with defined volume differences (ΔV ≈ 5, 3 and 2 cm2). Mean sensory rank was used as the primary perceptual metric. A multivariable Ordinary Least Squares regression model was developed and validated using Leave-One-Group-Out Cross-Validation across 35 independent sets.
RESULTS: A perceptual threshold of ΔV ≈ 3 cm2 was identified as the JND for hair volume. At ΔV ≈ 5 cm2, instrumental and sensory rankings were in strong agreement, whereas at ΔV ≈ 2 cm2, perceptual discrimination failed. Regression analysis confirmed bulk volume as the dominant predictor of perceived volume (β = 0.798, p < 0.001), with waviness providing a significant secondary contribution (β = 0.135, p = 0.005; Adjusted R2 = 0.660). A waviness correction factor (k ≈ 0.169) was derived, and cross-validation demonstrated robust predictive performance (66.67% top-rank accuracy; 94.87% top-two accuracy).
CONCLUSION: This study establishes the quantitative perceptual threshold for hair volume and introduces a morphology-aware correction model that aligns instrumental measurements with human perception. Together, these tools provide a practical framework for ensuring that volumising product claims are both physically measurable and perceptually meaningful.
METHODS: Bulk volume and waviness were quantified from hair swatches using a custom image-analysis framework. Volume was determined via pixel intensity-based fibre density classification, while waviness was derived from an amplitude-frequency analysis of the detrended medial axis. Sensory evaluation was conducted by an expert panel across controlled four-swatch sets with defined volume differences (ΔV ≈ 5, 3 and 2 cm2). Mean sensory rank was used as the primary perceptual metric. A multivariable Ordinary Least Squares regression model was developed and validated using Leave-One-Group-Out Cross-Validation across 35 independent sets.
RESULTS: A perceptual threshold of ΔV ≈ 3 cm2 was identified as the JND for hair volume. At ΔV ≈ 5 cm2, instrumental and sensory rankings were in strong agreement, whereas at ΔV ≈ 2 cm2, perceptual discrimination failed. Regression analysis confirmed bulk volume as the dominant predictor of perceived volume (β = 0.798, p < 0.001), with waviness providing a significant secondary contribution (β = 0.135, p = 0.005; Adjusted R2 = 0.660). A waviness correction factor (k ≈ 0.169) was derived, and cross-validation demonstrated robust predictive performance (66.67% top-rank accuracy; 94.87% top-two accuracy).
CONCLUSION: This study establishes the quantitative perceptual threshold for hair volume and introduces a morphology-aware correction model that aligns instrumental measurements with human perception. Together, these tools provide a practical framework for ensuring that volumising product claims are both physically measurable and perceptually meaningful.