Facial wrinkle categorization using Convolutional Neural Network


Submitted: 3 May 2024
Accepted: 2 September 2024
Published: 12 September 2024
Abstract Views: 1023
PDF: 49
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Authors

A tool for the detection and classification of wrinkles on the facial skin is always welcomed in the pursuit of tight and beautiful skin. If this tool involves the use of a state-of-the-art neural network and high-quality images, there is a high likelihood that it will be practical. Five thousand and ninety-eight images were categorized into four classes by a trained expert and prepared for neural network training. The task was to determine whether such prepared data could serve as good material for learning and whether they could provide sufficiently high accuracy in prediction. It was found that the answer to this question is positive.


References

Vasic C. Skin pore detection and classification using convolutional neural network. Australasian Journal of Dermatology 2024;65:178-81. DOI: https://doi.org/10.1111/ajd.14200

Day DJ, Littler CM, Swift RW, Gottlieb S. The Wrinkle Severity Rating Scale. Am J Clin Dermatol 2004;5:49-52. DOI: https://doi.org/10.2165/00128071-200405010-00007

Quatresooz P, Thirion L, Pierard-Franchimont C, Pierard GE. The riddle of genuine skin microrelief and wrinkles. International Journal of Cosmetic Science 2006;28:389-95. DOI: https://doi.org/10.1111/j.1467-2494.2006.00342.x

Setaro M, Sparavigna A. Irregularity skin index (ISI): a tool to evaluate skin surface texture. Skin Research and Technology 2001;7:159-63. DOI: https://doi.org/10.1034/j.1600-0846.2001.70303.x

Gormlev DE, Workman MS. Objective Evaluation of Methods Used to Treat Cutaneous Wrinkles. Clinics in Dermatology 1988;6:15-23. DOI: https://doi.org/10.1016/0738-081X(88)90027-2

Osman OF, Elbashir RMI, Abbass IE, et al. Automated Assessment of Facial Wrinkling: a case study on the effect of smoking. 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC) Banff Center, Banff, Canada, October 5-8, 2017. DOI: https://doi.org/10.1109/SMC.2017.8122755

Yap MH, Batool N, Ng C-C, et al. A Survey on Facial Wrinkles Detection and Inpainting: Datasets, Methods, and Challenges. IEEE Transactions on merging topics in computational intelligence, June.30.2021, doi: 10.1109/TETCI.2021.3075723 DOI: https://doi.org/10.1109/TETCI.2021.3075723

Jiang R, Kezele I, Levinshtein A, et al. A new procedure, free from human assessment that automatically grades some facial skin structural signs. Comparison with assessments by experts, using referential atlases of skin ageing. International Journal of Cosmetic Science 2019;41:67-78. DOI: https://doi.org/10.1111/ics.12512

Cula GO, Bargo PR, Kollias N. Assessing Facial Wrinkles: Automatic Detection and Quantification. Photonic Therapeutics and Diagnostics, Proc. of SPIE Vol. 7161 71610J-1, doi: 10.1117/12.811608 DOI: https://doi.org/10.1117/12.811608

Tan M, Le QV. EfficientNetV2: Smaller Models and Faster Training. Proceedings of the 38th International Conference on Machine Learning, PMLR 139, 2021.

Bora DJ. A Novel Approach for Color Image Edge Detection Using Multidirectional Sobel Filter on HSV Color Space. International Journal of Computer Sciences and Engineering 2017;5:2347-693.

Vasić, Čedomir. (2024). Facial wrinkle categorization using Convolutional Neural Network. Dermatology Reports. https://doi.org/10.4081/dr.2024.10034

Downloads

Download data is not yet available.

Citations