Your SlideShare is downloading. ×
0
Region Based Skin Color Detection
Region Based Skin Color Detection
Region Based Skin Color Detection
Region Based Skin Color Detection
Region Based Skin Color Detection
Region Based Skin Color Detection
Region Based Skin Color Detection
Region Based Skin Color Detection
Region Based Skin Color Detection
Region Based Skin Color Detection
Region Based Skin Color Detection
Region Based Skin Color Detection
Region Based Skin Color Detection
Region Based Skin Color Detection
Region Based Skin Color Detection
Region Based Skin Color Detection
Region Based Skin Color Detection
Region Based Skin Color Detection
Region Based Skin Color Detection
Region Based Skin Color Detection
Region Based Skin Color Detection
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

Region Based Skin Color Detection

581

Published on

Region based skin color detection.

Region based skin color detection.

Published in: Technology, Health & Medicine
0 Comments
1 Like
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total Views
581
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
21
Comments
0
Likes
1
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 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. 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. 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. 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. 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. 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. 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. 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. 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. 5.1 Region Extraction Region/Superpixel extraction – quick shift image segmentation using RGB color and positional (XY) coordinate
  • 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. 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. 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. 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. 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. 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. 6 Experimental Results Our proposed new region- based technique outperform current state-of-the–art technique
  • 18. 6 Experimental Results Applying CRF is always not good !
  • 19. 6 Experimental Results However, in aggregate CRF performs better!
  • 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. Thank you ! Questions ???

×