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Face Detection. Group 1: Gary Chern Paul Gurney Jared Starman. Our Algorithm. 4 Step Algorithm Runs in 30 seconds for test image. Region Finding and Separation. Maximal Rejection Classifier (MRC). Duplicate Rejection and “Gender Recognition”.

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Presentation Transcript
face detection

Face Detection

Group 1: Gary Chern

Paul Gurney

Jared Starman

our algorithm
Our Algorithm

4 Step Algorithm

Runs in 30 seconds for test image

Region Finding and Separation

Maximal Rejection Classifier (MRC)

Duplicate Rejection and “Gender Recognition”

Color Based Mask Generation

Input Image

3 d rgb color space
3-D RGB Color Space
  • Noticeable overlap between face and non-face pixels
  • Quantized RGB vectors from 0-63 (not 0-255)
probable face pixels
Probable Face Pixels
  • Lighter pixels mean higher probability of being a face pixel.
  • Filter with oval structuring element – removes background speckle.
color segmented mask
Color Segmented Mask
  • Mask produced from thresholding the filtered probability image
still have connected regions
Still have Connected Regions
  • Erosion and dilation separates most faces, but not all
  • Further processing is required
head and neck templates
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.
mrc model review
MRC Model-Review
  • As discussed in class, find projection of image set that minimizes # of non-faces selected
  • Gather lots of θ’s
mrc w out color segmentation
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
potential faces input to mrc
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.
output of mrc
Output of MRC

And it does!!!

duplicate rejection and gender
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.
results 1
Results (1)

Obstructed Face

We found all faces but one obstructed in this test image. Also found 1 female

results 2
Results (2)

Image #

#Faces Detected

#Faces in Image

Percentage

Correct

# Repeated Faces and False Positives

Bonus

1

20

21

95%

0

1

2

23

24

96%

0

1

3

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