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A Person and Context Specific Approach for Skin Colour Classification

Our innovative method adapts skin colour classification based on specific individuals and image conditions. By transforming parameters from high-level to low-level vision modules, our approach ensures robust results for diverse scenarios. With a focus on promoting adaptive classifiers, we achieve precise detection and facial feature recognition in images. The process involves learning and calculating rules through machine learning techniques, leading to improved accuracy and efficiency in skin colour classification tasks. Future plans include developing additional adaptive colour classifiers for various facial features.

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A Person and Context Specific Approach for Skin Colour Classification

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  1. A Person and Context Specific Approach for Skin Colour Classification

  2. motivationour approach results outlook Motivation simple way: promote parameters: • high-level to low-level vision module • mathematically transform parameters • Our scenario: Adaptive Skin Colour Classification • Classifier adapts to person and context see: A.E. Broadhurst, S. Baker: Setting Low-Level Vision Parameters, CMU-RI-TR-04-20, Robotics Institute, Carnegie Mellon University, 2004. Technische Universität München Matthias Wimmer

  3. motivationour approach results outlook Motivation Technische Universität München Matthias Wimmer

  4. motivationour approach results outlook Observations • Skin colour depends on image conditions: • illumination: light source, light colour, shadow, shading,… • camera: type, settings,… • visible person: ethnic group, tan,… • Skin colour occupies a large area within colour space • Skin colour varies greatly between images. • Skin colour varies slightly within an image. image 1 image 2 green green red red skin colour pixels (red) and other pixels (blue), static skin colour clusters (white), adaptive skin colour clusters (yellow) Technische Universität München Matthias Wimmer

  5. motivationour approach results outlook Our Approach Online steps: • Step 1: detect the image specific skin colour • using the face detector • using the skin colour mask • Step 2: calculate the input parameters • Step 3: adapt the skin colour classifier Offline step: • learn a mask that extracts skin colour pixels • specific for the face detector Technische Universität München Matthias Wimmer

  6. motivationour approach results outlook Learn the Calculation Rules specify these (ground truth) specify these (ground truth) learn those • Gather many training images • Manually annotate images with ground truth • Learn calculation rules via machine learning techniques • e.g. linear regression, neural networks, model trees, … Technische Universität München Matthias Wimmer

  7. motivationour approach results outlook Results • good robustness for • coloured persons • exact shape outline • detection of facial parts:eyes, lips, brows,… original image fixed parameters adaptive parameters • correctly detected pixels: • fixed parameters: 90.4% 74.8% 40.2% • adaptive parameters: 97.5% 87.5% 97.0% • improvement: 0.08 0.17 1.41 Technische Universität München Matthias Wimmer

  8. motivationour approach results outlook Outlook original fixed adaptive • We will create further adaptive colour classifiers • lip • teeth • eyes • brows, hair • … • Preliminary results for lip colour classifier: Technische Universität München Matthias Wimmer

  9. Thank you! Technische Universität München Matthias Wimmer

  10. challengeour approach results outlook Motivation Skin colour detection supports… • face model fitting • mimic recognition • person identification • gaze estimation • fatigue detection (e.g. vehicle) • hand tracking • gesture recognition • action recognition • supervising work Technische Universität München Matthias Wimmer

  11. challengeour approach results outlook Challenge • Skin colour depends on image conditions: • illumination: light source, light colour, shadow, shading,… • camera: type, settings,… • visible person: ethnic group, tan,… • Skin colour occupies a large area within colour space Technische Universität München Matthias Wimmer

  12. challengeour approach results outlook Challenge (2): non-skin colour pixels • Skin colour pixels have to be separated from non-skin colour pixels. • Areas of skin colour and non-skin colour overlap. • Colour can not make a distinctive separation. Technische Universität München Matthias Wimmer

  13. challengeour approach results outlook Our approach Offline step: • learn the skin colour mask • specific for the face detector Online steps: • Step 1: detect the image specific skin colour model • using the face detector • using the skin colour mask • Step 2: adapt a skin colour classifier • Step 3: calculate the skin colour image Technische Universität München Matthias Wimmer

  14. challengeour approach results outlook Offline: Learn the skin colour mask 1. 2. 3. • face image database with labeled skin colour pixels • skin colour mask: array with 24 x 24 cells Computational steps: • detect the face in every image • every cell is assigned the relative number of labeled skin colour pixels at its position • apply threshold Technische Universität München Matthias Wimmer

  15. challengeour approach results outlook Step 1: Detect the image specific skin colour model • detect the face • extract the skin colour pixels • normalized RGB colour space: base = R + G + B r = R / base g = G / base • skin colour model: • mean values: μr, μg, μbase • standard deviations: σr, σg, σbase Technische Universität München Matthias Wimmer

  16. challengeour approach results outlook Step 2: Adapt a skin colour classifier • non-adaptive skin colour classifier: skin := 0.35 ≤ r ≤ 0.5 0.2 ≤ g ≤ 0.7 200 ≤ base ≤ 740 • adaptive skin colour classifier: skin := lowr ≤ r ≤ highrlowg ≤ g ≤ highglowbase ≤ base ≤ highbase • learn the bounds via the skin colour model • mean value and standard deviationlowr := μr – 2σrhighr := μr + 2σr . . . . . . . . . • linear function:lowr := aμr + bμg + cμbase + dσr + eσg + fσbase + g . . . Technische Universität München Matthias Wimmer

  17. challengeour approach results outlook Related work • Feedback of information fromhigh level vision components to low level vision components Technische Universität München Matthias Wimmer

  18. challengeour approach results outlook Conclusion • Challenge: much variation within skin colour • illumination, camera, visible person • skin colour occupies a large area within colour space • We propose a way to reduce those variations • exploit an image specific skin colour model • adapt a skin colour classifier to that skin colour model • We proved our approach • using a simple but real-time capable skin colour classifier • comparison: non-adaptive ↔ adaptive Technische Universität München Matthias Wimmer

  19. challengeour approach results outlook Ongoing research • Learn skin colour mask for other face detectors • Specialize more powerful skin colour classifiers • Recognize other feature images/colour images • lip colour image • tooth colour image • eye colour image • hair colour image • eye brow colour image example: lip colour detection Technische Universität München Matthias Wimmer

  20. Adaptive skin colour classifier • non adaptive skin colour classifier: skin := 0.35 ≤ r ≤ 0.5  0.2 ≤ g ≤ 0.7  200 ≤ base ≤ 740 • adaptive skin colour classifier: skin := lowr ≤ r ≤ highr lowg ≤ g ≤ highg lowbase ≤ base ≤ highbase • learn the bounds out of the skin colour model Technische Universität München Matthias Wimmer

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