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Illumination-invariant face skin pigmentation prediction
Thesis (MEng)--Stellenbosch University, 2024.
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| Format: | Thesis |
| Language: | en_ZA en_ZA |
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Stellenbosch : Stellenbosch University
2024
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| _version_ | 1867613755098005504 |
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| access_status_str | Open Access |
| author | Mbatha, Success Katlego |
| author2 | Theart, Rensu |
| author_browse | Mbatha, Success Katlego Theart, Rensu |
| author_facet | Theart, Rensu Mbatha, Success Katlego |
| author_sort | Mbatha, Success Katlego |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Thesis (MEng)--Stellenbosch University, 2024. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/130412 |
| institution | Stellenbosch University (South Africa) |
| language | en_ZA en_ZA |
| last_indexed | 2026-06-10T12:41:10.692Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/130412 Illumination-invariant face skin pigmentation prediction Mbatha, Success Katlego Theart, Rensu Booysen, Thinus Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering. Neural networks (Computer science) Image processing -- Digital techniques Human face recognition (Computer science) Image segmentation Human skin color UCTD Thesis (MEng)--Stellenbosch University, 2024. ENGLISH ABSTRACT: Skin tone estimation is a critical task with a wide range of applications in fields such as cosmetic science, dermatology, image processing, and facial recognition. Accurate skin tone estimation plays a significant role in improving the inclusivity and fairness of these systems. As machine learning and artificial intelligence continue to advance and finds application in widely used systems, addressing the challenges of skin tone estimation has become increasingly important to ensure that these technologies perform consistently across diverse skin tones. This study focuses on contributing to this field of study by developing a CNN based skin tone classification/estimation model capable of delivering consistent accuracy for individuals with varying skin tones and under diverse lighting conditions. Early in 2022, Google introduced a new skin tone classification system known as the “Monk Skin Tone (MST)” which offers a broader range of skin tones compared to the commonly used “Fitzpatrick Skin Type (FST)” system. This more comprehensive scale is designed to be inclusive and representative of the diverse global population and has been adopted in this study. A data collection campaign was hosted at Stellenbosch University with an aim to develop a dataset with a diverse range of skin tones, classified into the MST, addressing the shortcomings of existing openly accessible datasets. This effort resulted in the acquisition of 21 375 images from 285 participants. Furthermore, the model development process involves exploring various CNN architectures, model configurations, and data pre-processing techniques to maximise the accuracy of skin tone estimation. Experimental findings on various model configurations are summarised as follows: The regression model trained on LAB images, which was selected as the best performing model, demonstrated the highest accuracy at 58.12%. In contrast, a pre-trained CNN model showed limited accuracy, achieving a modest 36.85%. When colour balancing techniques were applied to the dataset images, the resulting accuracy was 55.05%, falling short of the performance of the regression model using LAB images. Moreover, an attempt to increase both image and model dimensionality by converting images to RGB-LAB-HSV led to overfitting issues, resulting in an accuracy of 45.76%. Furthermore, the study explored the model’s performance under different lighting conditions, with the highest accuracy recorded under warmer lighting conditions such as “Halogen warm white” (62.07%), and “Florescent warm white” (58.37%). The study also discusses the impact of spectral characteristics on lighting conditions, particularly noting that “LED warm white” exhibited lower accuracy at 55.73%. This reported accuracy is of samples at distance ≤ 0.5 units from the actual targets. However, when the margin distance is increased to 1 and 2 units, the average accuracy across all light types becomes (85.45 }2.01) % and (97.16 }1.00) %. In the evaluation of skin tone estimation accuracy based on Monk skin tones in the LAB space, the study found that Group 1, representing individuals with lighter skin tones (Monk skin tone 1-3), exhibited strong accuracy. Group 2, encompassing individuals with middle-range skin tones (Monk skin tones 4-7), displayed comparatively lower accuracy in the skin tone estimation. Group 3, consisting of individuals with higher pigmentation skin tones, fell between the performance levels of Groups 1 and 2 in terms of accuracy. Despite the non-linear performance observed across all these skin tone groups, the overall skin tone estimation performance is satisfactory, with an average predicted-to-target error distance value of 16.40 } 20.62 in the LAB space for all samples. Overall, this research contributes to the advancement of skin tone estimation, with practical implications for enhancing the performance of facial analysis algorithms in real-world applications. AFRIKAANSE OPSOMMING: Geen opsomming beskikbaar. Masters 2024-02-28T07:43:56Z 2024-04-26T16:40:10Z 2024-02-28T07:43:56Z 2024-04-26T16:40:10Z 2024-03 Thesis https://scholar.sun.ac.za/handle/10019.1/130412 en_ZA en_ZA Stellenbosch University xv, 125 pages : illustrations. application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Neural networks (Computer science) Image processing -- Digital techniques Human face recognition (Computer science) Image segmentation Human skin color UCTD Mbatha, Success Katlego Illumination-invariant face skin pigmentation prediction |
| title | Illumination-invariant face skin pigmentation prediction |
| title_full | Illumination-invariant face skin pigmentation prediction |
| title_fullStr | Illumination-invariant face skin pigmentation prediction |
| title_full_unstemmed | Illumination-invariant face skin pigmentation prediction |
| title_short | Illumination-invariant face skin pigmentation prediction |
| title_sort | illumination invariant face skin pigmentation prediction |
| topic | Neural networks (Computer science) Image processing -- Digital techniques Human face recognition (Computer science) Image segmentation Human skin color UCTD |
| url | https://scholar.sun.ac.za/handle/10019.1/130412 |
| work_keys_str_mv | AT mbathasuccesskatlego illuminationinvariantfaceskinpigmentationprediction |