mlsanityData to 7 Oct 2026

Color Invariant Skin Segmentation

arXiv 2204.09882 ↗First posted 21 Apr 2022cs.CVcited by 3

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Abstract (arXiv)

This paper addresses the problem of automatically detecting human skin in images without reliance on color information. A primary motivation of the work has been to achieve results that are consistent across the full range of skin tones, even while using a training dataset that is significantly biased toward lighter skin tones. Previous skin-detection methods have used color cues almost exclusively, and we present a new approach that performs well in the absence of such information. A key aspect of the work is dataset repair through augmentation that is applied strategically during training, with the goal of color invariant feature learning to enhance generalization. We have demonstrated the concept using two architectures, and experimental results show improvements in both precision and recall for most Fitzpatrick skin tones in the benchmark ECU dataset. We further tested the system with the RFW dataset to show that the proposed method performs much more consistently across different ethnicities, thereby reducing the chance of bias based on skin color. To demonstrate the effectiveness of our work, extensive experiments were performed on grayscale images as well as images obtained under unconstrained illumination and with artificial filters. Source code: https://github.com/HanXuMartin/Color-Invariant-Skin-Segmentation

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 21 of its 90 references that are in the map. First posted on arXiv 21 Apr 2022.

Builds on

21 of 90 references.

References · 21

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9 more
  1. 2019REPAIR: Removing Representation Bias by Dataset Resamplingcited by 157
  2. 2020Demographic Bias in Biometrics: A Survey on an Emerging Challengecited by 93
  3. 2020Regularizing Deep Networks with Semantic Data Augmentationcited by 73
  4. 2020FairFace Challenge at ECCV 2020: Analyzing Bias in Face Recognitioncited by 26
  5. 2017Human Skin Detection Using RGB, HSV and YCbCr Color Modelscited by 16
  6. 2014A Fusion Approach for Efficient Human Skin Detectioncited by 5
  7. 2019Hand Segmentation and Fingertip Tracking from Depth Camera Images Using Deep Convolutional Neural Network and Multi-task SegNetcited by 3
  8. 2019Semi-supervised Skin Detection by Network with Mutual Guidancecited by 3
  9. 2019A deep learning model for segmentation of geographic atrophy to study its long-term natural historycited by 3

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Cited by year

Citing papers per year: 2022: 1; 2023: 1; 2024: 1.

3 papers.

  1. 2023A Web-based Mpox Skin Lesion Detection System Using State-of-the-art Deep Learning Models Considering Racial Diversitycited by 16
  2. 2022Casual Conversations v2: Designing a large consent-driven dataset to measure algorithmic bias and robustnesscited by 5
  3. 2024Ensembling convolutional neural networks for human skin segmentationcited by 0