Color Segmentation

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Color Segmentation

• View the YIQ color space
• -Yluminance, Ihue, Qsaturation
• Human skin occupy a small portion of the I and Q
  spaces.
• From training images, compare and contrast hue
  and saturation of
• faces only vs. entire image

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Hue and Saturation


Faces
Training Image
Q Distribution
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Mask After Color Segmentation

• Skin elements remain.
• Holes in faces later eliminated with hole-filling

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Mask After Object Removal
Based on size distribution of remaining objects,
remove small ones
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Correlation Template Matching I Average Face

• First attempt Average face
• Taking average of all faces from ground truth
  masks
• Results Less than satisfactory.
• Face with distinguishing features blurred
• Correlation separation is not high, identifies
  many skin color regions (clothing, background) as
  false positives.

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Correlation Template Matching II Edge detection

• After color segmentation, most remaining regions
  are composed of skin-color tones.
• Distinguishing features resides in edges
• Use Canny edge filter on black-white images for
  extraction
• Composed average face using edges, scaled to mean
  zero

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Correlation comparison

• Average face template
• Poor separation between faces
• Difficult to identify face centroid

• Edge face template
• Better separation between faces
• Peaks (centroid) more easily identifiable

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Region counting - Supplementary method

• The edge outlines have clearly identifiable
  connected regions
• Can be counted, and statistics used to help
  reject clutter

Number of regions 14
Number of regions 43
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Detection Algorithm

• Correlation Degree of matching
• Dimensions height, width
• Region counting complexity of image

Single face
Multiple faces
Correlation
Dimensions
Region counting
Multi-face detection
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Multiple Faces within a Single Region

• Search for peaks in correlation
• A single face may give multiple peaks
• Estimate expected number of faces within Region
• Do not want repeats

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Find Largest Peak

• Find largest peak in correlation
• Location of first peak
• Exclude area of radius R (about peak) from rest
  of search
• R determined dynamically from size of region and
  number of expected faces

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Next Peak

• Find next largest peak
• Exclude area (of radius R) surrounding both peaks
  from further search
• Continue search in this manner until desired
  number of peaks found

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Find Multiple Faces

• Stop search if there are no more peaks to be
  found
• (Number of peaks found can be fewer than
  estimate)
• Each peak location corresponds to face center
  location

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Conclusion

• Reasonably successful performance
• Misses
• False positives/repeats
• Algorithm relies heavily on Color Segmentation
  and Edge Extraction
• Difficulty with closely-spaced faces
• Separation
• Detecting multiple faces in single region
  (correct estimate)

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