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Statistical Approach to a Color-based F ace Detection Algorithm

Statistical Approach to a Color-based F ace Detection Algorithm. EE 368 Digital Image Processing Group 15 Carmen Ng, Thomas Pun May 30, 2002. Statistical Approach to a Color-based Face Detection Algorithm. Assumptions 4 Stages: Pre-processing Skin Color Region Labeling

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Statistical Approach to a Color-based F ace Detection Algorithm

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  1. Statistical Approach to a Color-based Face Detection Algorithm EE 368 Digital Image Processing Group 15 Carmen Ng, Thomas Pun May 30, 2002

  2. Statistical Approach to a Color-based Face Detection Algorithm • Assumptions • 4 Stages: • Pre-processing • Skin Color Region Labeling • Statistical Face Selection Techniques • Edge Detection • Advantages/Disadvantages

  3. Statistical Approach to a Color-based Face Detection Algorithm Assumptions: • Color image • Multiple faces with similar area • Face orientation

  4. Statistical Approach to a Color-based Face Detection Algorithm I . Image Pre-processing • Boundary extension • Improves accuracy

  5. Statistical Approach to a Color-based Face Detection Algorithm II . Skin Color Region Labeling • Color-based • Chrominance extraction in YCbCr space • Morphological operations • Dilation and erosion

  6. <= Original Image Rough Mask =>

  7. Binary Mask after Morphological Operations

  8. Statistical Approach to a Color-based Face Detection Algorithm III . Statistical Analysis • Popular area finder • Facial feature detector (holes in binary images) • Popular area, width and height • Face rejection • Reject unpopular areas

  9. Selected Face Regions after Stage III

  10. Statistical Approach to a Color-based Face Detection Algorithm IV . Facial Feature (Eye) Detection • Approximate eye location • LPF to remove noise • Edge detection to locate strong edges

  11. Typical Background Typical Face After LPF and Edge Detection

  12. Statistical Approach to a Color-based Face Detection Algorithm Results/Conclusions: • 88% success rate • Adv: fast, no training required,work with video compression std. • DisAdv: min of faces required in image, work best with reliable facial detector

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