Computational Cosmetic Quality Assessment of Human Hair in Low Magnifications

We take advantage of human hair-specific geometry to visualize sparse submicron and micron-sized cuticle peelings with imaging dark-field scattering at highly oblique tip-side illumination. The paper shows that the statistics of these features can directly estimate hair quality is much lower magnifications (down to
$20\times $
) with less powerful objectives when the features themselves are significantly below the system resolution. Our technique for quality categorization of black, blond, and grey human scalp hair samples is successful in detecting healthy and damaged hair in all cases by a large margin (factor of 5 contrast in proposed metric). As demonstrated, the proposed metric even has a strong correlation with the type of damage such as ironing, discoloration, and UV (ultraviolet) exposure. Therefore, this technique has a strong potential for lower cost, portable, and automatic hair diagnostic apparatuses.