ee368group01.ppt
Transcript of ee368group01.ppt
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Face Detection
Group 1: Gary Chern
Paul Gurney
Jared Starman
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Input Image
Color Based Mask
Generation
Region Finding and Separation
Maximal Rejection Classifier (MRC)
Duplicate Rejection and
“Gender Recognition”
Our Algorithm
• 4 Step Algorithm
• Runs in 30 seconds for test image
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3-D RGB Color Space
• Noticeable overlap between face and non-face pixels• Quantized RGB vectors from 0-63 (not 0-255)
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Probable Face Pixels
• Lighter pixels mean higher probability of being a face pixel.• Filter with oval structuring element – removes background speckle.
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Color Segmented Mask
• Mask produced from thresholding the filtered probability image
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Still have Connected Regions
• Erosion and dilation separates most faces, but not all• Further processing is required
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Head and Neck Templates
• To separate faces, convolve regions with head-and-neck templates.• Find locations with highest correlation, remove region, and repeat.• Repeat with several sized head-and-neck templates.
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MRC Model-Review
• As discussed in class, find projection of image set that minimizes # of non-faces selected• Gather lots of θ’s
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MRC w/out Color Segmentation
• Computationally more intensive
• Training wasn’t perfect so we still get non-faces
•False detections usually aren’t face-colored in MRC
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Potential Faces Input to MRC
• Our idea: Just do MRC on color-segmented/separated regions• Notice bag of oranges and two roof pictures are the only non-face inputs.• MRC only has to remove those 3 pictures.
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Output of MRC
And it does!!!
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Duplicate Rejection and Gender
• If two detected faces are too close, we throw out the second face.• We search for the lowest average valued (darkest) detected face and label that as female.
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We found all faces but one obstructed in this test image. Also found 1 female
Results (1)
Obstructed Face
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Image # #Faces Detected #Faces in Image PercentageCorrect
# Repeated Faces and False Positives
Bonus
1 20 21 95% 0 12 23 24 96% 0 13 25 25 100% 0 0
4 23 24 96% 0 0
5 21 24 88% 0 0
6 23 24 96% 0 0
7 22 22 100% 0 0
Results (2)
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Gender RecognitionFace Detection
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Gender RecognitionFace Detection