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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