Region Based Skin Color Detection

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Region based skin color detection.

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Region Based Skin Color Detection

  1. 1. Region-based Skin Color Detection Rudra P K Poudel (Presenter), Hammadi Nait-Charif, Jian Jun Zhang Media School, Bournemouth University, UK David Liu Siemens Corporate Research, USA
  2. 2. Outline of the talk 1. Introduction- skin color detection 2. Literature Review 3. Current Problems 4. Region-Based Technique 5. Proposed Region-Based Technique 6. Experimental Results 7. Conclusions
  3. 3. 1. Introduction • Task: separate skin and non-skin regions (not pixels) • Motivation: invariant of rotation, scaling and occlusion • Problems: illumination, ethnicity background, make-up, hairstyle, eyeglasses, background color, shadows, motion illumination, skin look like colors, etc. Source: Harry Potter movie
  4. 4. 1.1 Applications • Hand tracking, face detection, pornography detection, person tracking • Skin color detection module equally applicable for other color editing, detection etc applications • Color is used as primary clue in many image processing and computer vision applications
  5. 5. 2. Literature Review 2.1 Color space • RGB • HSV • YCbCr • Perceptually uniform color systems (CILLAB, CIELUV, LAB) • Normalized RGB 2.2 Skin color classifier • Nonparametric methods: histogram, Bayes classifier, self-organizing map • Parametric methods: single Gaussian, mixture of Gaussian • Others: neural network
  6. 6. 2. Literature Review Summary: • Color space: RGB and HSV are two widely used techniques • Classification method: Naïve Bayes classifier and mixture of Gaussian are widely used techniques • Gaussian model: need few training data, difficulties on parameter tuning, need less memory space, processing/detection slow • Bayes theorem: need for larger training data, easy for learning, need more memory space, processing/detection fast State-of-the-art method: • Jones, M. J. and Rehg, J. M. (2002). Statistical color models with application to skin detection. International Journal of Computer Vision, 46(1):81–96.
  7. 7. 3. Current Problem • Probability accumulation for higher level vision task- as probability for skin/non-skin vary highly even for adjacent pixels Naturally skin is continuous region
  8. 8. 4 Region Based Approach • Yang and Ahuja (1998) and Kruppa et al. (2002)- search elliptical regions for face detection • Sebe Sebe et al.(2004)- 3x3 fixed size patches to train Bayesian network • Our approach treat skin as region with varying sizes, which is purely based on image evidence
  9. 9. Proposed Technique/Framework 1. Region extraction- quick shift image segmentation, also know as “superpixels” 2. Region classification- pixel/region based 3. Smoothing- Conditional Random Field (CRF) 5. Proposed Region-Based technique
  10. 10. 5.1 Region Extraction Region/Superpixel extraction – quick shift image segmentation using RGB color and positional (XY) coordinate
  11. 11. 5.1 Region Extraction - Region extraction is purely evidence based i.e. based on RGB color and spatial location (xy-coordinate) of the image - Regions have different size and shape, which is depend upon complexity of the image - No explicit concept of boundary - Quick shift preserve the boundary of the objects, hence we could get very accurate object segmentation - We could set importance on color difference vs spatial distance
  12. 12. 5.2 Region Classification 5.2.1 Basic Skin Color Classifer Naïve Bayes: posterior likelihood * prior∝ However, we could use any suitable/best method for skin classification )( )()/( )/( cp spscp csp = )( )()/( )/( cp nspnscp cnsp = Θ> )/( )/( cnsp csp Θ> )()/( )()/( nspnscp spscp 1 )/( )/( > nscp scp Where, c = color, s = skin and ns = non-skin
  13. 13. 5.2 Region Classification • Average the skin probability (s) of all color pixels (c) belongs to the given superpixel (sp) • Average the non-skin probability (ns) of all color pixels (c) belongs to the given superpixel (sp) ∑= N i icsP N spsP )|( 1 )|( ∑= N i icnsP N spnsP )|( 1 )|(
  14. 14. 5.3 Smoothing with CRF • Conditional Random Field (CRF) optimization equation • Color potential • Edge and boundary potential ∑∑ ∈∈ Φ+Ψ−=− Ess jiji Ss ii jii ssccslSLP ),( ),|,()|());|(log( ωω ))|(log()|( iiii slPsl =ψ [ ]ji ji ji jiji cc ss ssL sscc ≠         −+ =Φ , ||||1 ),( ),|,(
  15. 15. 5.4 Training First Phase (training histogram): • Train 2 histograms for skin and non-skin separately Second phase (training CRF): learning : si sj … px1(s|c) px1(ns|c) px1(s|c) px1(ns|c) color difference + boundary length ∑∑ ∈∈ Φ+Ψ−=− Ess jiji Ss ii jii ssccslSLP ),( ),|,()|());|(log( ωω ω
  16. 16. 6 Experimental Results • Dataset content 14 thousands images collected freely from the web (Compaq dataset) • 4,700 are skin and 9,000 non-skin images • Approximately 1 billion pixels are manually labeled • 50% is use for training and 50% for testing Method True Positive False Positive Jones and Rehg (2002) 90% 14.2% Our (Superpixel only) 91.44% 13.73% Our (Superpixel and CRF) 91.17% 13.12%
  17. 17. 6 Experimental Results Our proposed new region- based technique outperform current state-of-the–art technique
  18. 18. 6 Experimental Results Applying CRF is always not good !
  19. 19. 6 Experimental Results However, in aggregate CRF performs better!
  20. 20. 7. Conclusions • Region-based technique performs better than pixel-based • Region-based technique could easily incorporate texture info and other type of features to improve the result • Aggregation of pixels into region help to reduce local redundancy. • Region-based technique extracts larger smooth regions, which is very helpful for higher-level vision task The message to take home: It is better/natural to treat skin as regions instead of individual pixels!
  21. 21. Thank you ! Questions ???

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