Computational Cosmetic Quality Assessment of Human Hair in Low Magnifications
Name
08752218.pdf
Description
Published version
Size
16.08 MB
Format
Adobe PDF
Checksum (MD5)
f57d4a8285d18558de7c07aaf4a2b382
Author(s)
Heshmat, Barmak
Rastogi, Krishna
Raskar, Ramesh
Lee, Ik Hyun
Date Issued
2019
Journal
IEEE Access
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Version
Final published version
Abstract
© 2019 IEEE. 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×) 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.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ACCESS.2019.2926139
