Bridging the gap between consumer perception and objective measurements: A novel multiview image analysis methodology...

While individual hair parameters can be objectively assessed at the fiber level, quantifying total hair volume in a 3D context remains a challenge despite its primary importance for consumer perception. This paper describes an automated methodology based on image analysis, specifically designed to provide instrumental measurements that are both inspired by and predictive of consumer evaluation.

The method was developed through a multi-stage scientific approach: (1) identifying consumer-relevant volume attributes (root lift, volume along the hair length and perceived mass) through qualitative interviews, (2) establishing a standardized image acquisition protocol with a database of 163 women and (3) developing image analysis algorithms focused on Regions of Interest (ROI) used by consumers. The visual perception of hair volume was quantified by a panel of 90 non-trained volunteers using a paired comparison scoring method to create a robust visual referential.

To validate the tool, a hybrid study was conducted on 40 women (20 seeking volume increase, 20 seeking volume reduction). The results demonstrate that these image analysis algorithms are highly effective at measuring product performance (p-value ≤0.022). Furthermore, strong correlations were found between instrumental data, sensory expert ratings and consumer self-assessments (e.g., 0.65 to 0.88 for root lift).

This methodology offers a robust, repeatable and standardized framework for evaluating hair product efficacy in alignment with real-world consumer perception.