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Color Segmentation. View the YIQ color space: -Y=luminance, I=hue, Q=saturation 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. Hue and Saturation. Faces. Training Image. - PowerPoint PPT Presentation
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Color Segmentation
• View the YIQ color space:-Y=luminance, I=hue, Q=saturation
• 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
Hue and Saturation
-150 -100 -50 0 50 100 1500
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5Histogram of Q Components of Training
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Q DistributionTraining Image Faces
• Skin elements remain.
• Holes in faces later eliminated with hole-filling
Mask After Color Segmentation
Mask After Object Removal
Based on size distribution of remaining objects, remove small ones
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
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
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
Detection Algorithm– Correlation – Degree of matching
– Dimensions – height, width
– Region counting – complexity of image
Correlation Dimensions Region counting
Correlation Dimensions Region countingMulti-face detection
Single face
Multiple faces
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
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
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
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
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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Gender RecognitionFace Detection
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Gender RecognitionFace Detection