Hair Tone Estimation at Roots via Imaging Device with Embedded Deep Learning

January 2019 in “ Electronic Imaging ”

Panagiotis‐Alexandros Bokaris, Emmanuel Malherbe, Thierry A. W. Wasserman, Michael A. Haddad, Matthieu Perrot

hair tone estimation Convolutional Neural Network CNN macro images image processing hair coloration beauty personalization clinical evaluation

Studysummary This study found that a lightweight Convolutional Neural Network model can accurately and quickly determine natural hair tone from high-resolution images of hair roots, outperforming other popular methods.

Automatically generated from the study's abstract, not written by a person, and not a review of the full paper. Not medical advice or a treatment recommendation. Read the original study, and consult a qualified healthcare professional before changing treatment. Full disclaimer ·

View this study on library.imaging.org →

Discuss this study in the Community →

Similar Research

5 / 1000+ results

research Hair Color Digitization through Imaging and Deep Inverse Graphics

February 2022 in “arXiv (Cornell University)”

This study introduces a novel method for capturing and digitally rendering the color appearance of physical hair samples using deep neural networks.

research Hair color digitization through imaging and deep inverse graphics

1 citations , January 2022 in “Electronic Imaging”

This study introduces a novel method for digitizing hair color that accurately captures and renders the color appearance of physical hair samples in synthetic images.

research Convolutional Neural Networks for Non-Invasive Diagnosis of Androgenetic Alopecia using Dermoscopic Hair Images

August 2024

This study developed a convolutional neural network model for non-invasive diagnosis of androgenetic alopecia using dermoscopic images, demonstrating potential accuracy and scalability while highlighting the importance of model interpretability for clinical use.

research Predicting Hair Loss with AI: A Deep Learning Framework Combining Genetic and Scalp Health Data

2 citations , January 2024

In this study, researchers proposed a deep learning approach combining genetic, hormonal, scalp health, and lifestyle data to predict hair loss, employing CNNs for image analysis and RNNs for modeling data over time, although specific results are not reported.

research Hair and Scalp Disease Detection using Machine Learning and Image Processing

5 citations , December 2022 in “arXiv (Cornell University)”

This study used a deep learning approach that successfully predicts alopecia, psoriasis, and folliculitis with a 2D convolutional neural network, achieving a training accuracy of 96.2% and validation accuracy of 91.1%.