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Face Detection Project

Face Detection Project. Nitin Kartik nitin.kartik@motorola.com EE368 Group 24 Spring 2002, Stanford University Prof. Bernd Girod. Algorithm Design. Seconds. 15. Sub-Sample Color Rejection Black-and-White Correlation Coefficient Centroids. 2. 2. 52. 1. Step 1: Sub-sample.

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Face Detection Project

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  1. Face Detection Project • Nitin Kartiknitin.kartik@motorola.com • EE368 Group 24 • Spring 2002,Stanford University • Prof. Bernd Girod 1

  2. Algorithm Design Seconds 15 • Sub-Sample • Color Rejection • Black-and-White • Correlation Coefficient • Centroids 2 2 52 1 2

  3. Step 1: Sub-sample • 10x10 Reduction • Averaging over pixels(Uniform Filter) • Reduce Processing 3

  4. Step 2: Color Rejection • Trained from Examples • Color Ratios • Min/Max Table Ratio Min Max Red : Green 1.000 4.500 Green : Blue 0.843 12.000 Blue : Red 0.0185 1.000 4

  5. Step 3: Black-and-White • No need for Color any more • Average Red, Green, Blue Components • Further Reduce Processing 5

  6. Step 4: Correlation Coefficient • Face Template (Averages) • Compute Pixel-wise Correlation Coefficient • Some Falsely Detected Faces 6

  7. Ideal Classifier Practical Classifier Face Face Non-Face Non-Face Threshold Threshold Step 4: Correlation Coefficient(False Alarms) • Practical Classifiers 7

  8. Step 5: Determine Centroids • Hunting Algorithm • Min / MaxWidth / Height • Centroid = (midX, midY) 8

  9. Discarded Alternatives • Sub-sampling with other factors • Erosion / Dilation • RMS-Based Rejection 9

  10. Face Detection Project • Nitin Kartiknitin.kartik@motorola.com • EE368 Group 24 • Spring 2002,Stanford University • Prof. Bernd Girod 10

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