Automatic Face Recognition Using Color Based Segmentation and Intelligent Energy Detection

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Automatic Face Recognition Using Color Based
Segmentation and Intelligent Energy Detection

• Michael Padilla and Zihong Fan
• Group 16
• EE368, Spring 2002-2003

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Project Objective
Given a digital image of attractive and
intelligent EE368 students and teaching staff,
detect the presence of faces in the image and
output their location and (if poss.) gender.
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Basic System Summary

• Initial Design

• Reduced Eigenface-based coordinate system
  defining a face space, each possible face a
  point in space.
• Using training images, find coordinates of
  faces/non-faces, and train a neural net
  classifier.
• Abandoned due to problems with neural network
  lack of transparency, poor generalization.
• Replaced with our secondary design strategy

• Final System

Color-space Based Segmentation
Morphological Image Processing
Face Estimates
Matched Filtering
Peak/Face Detector
Input Image
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H vs. S vs. V (Face vs. Non-Face)
For faces, the Hue value is seen to typically
occupy values in the range H lt 19 H gt 240 We use
this fact to remove some of the non-faces pixels
in the image.
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Y vs. Cr vs. Cb
In the same manner, we found empirically that for
the YCbCr space that the face pixels occupied the
range 102 lt Cb lt 128 125 lt Cr lt 160 Any other
pixels were assumed non-face and removed.
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R vs. G vs. B
Finally, we found some useful trends in the RGB
space as well. The Following rules were used to
further isolate face candidates 0.836G 14 lt B
lt 0.836G 44 0.89G 67 lt B lt 0.89G 42
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Removal of Lower Region Attempt to Avoid
Possible False Detections
Just as we used information regarding face color,
orientation, and scale from The training images,
we also allowed ourselves to make the assumption
that Faces were unlikely to appear in the lower
portion of the visual field We Removed that
region to help reduce the possibility of false
detections.
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Morphological ProcessingStep 1 Open Operation
After removing pixels based on color space
considerations, removed specs initially by use of
the open operation with a window of size 3x3.
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Morphological ProcessingStep 2 Small Blob
Removal
Model the average size of head blobs in the
training reference image. Remove blobs below one
standard deviation.

• In addition, we
• Convert to grayscale. In our case, no more color
  information to extract.
• Apply mean removalhistogram equalization -gt
  flatten and bring out details.

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Template Design

• Manually selected a number of quality faces
  centered, straight, lighting, diverse.

• Measured face dimensions and used Matlab to
  uniformly scale and align them.

• Efforts resulted in 26 sample faces added to
  produce the final template.

Final Face Template
Original Faces
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Matched Filter Operation
While (remaining mask area to analyze)
for s 1S scale for r 1R
rotation for thrshld
MaxMin template
temp(mother_temp, s, r)
peaks conv(mask_image, template)
face detector(peaks, thrshld)
if (face)
adjust mask_image
adjust remaining mask area

Scale and Rotate
Face Coordinates
Masked Input Image
Apply Matched Filter
Compare peaks To Threshold, T(n)
If Peak gt Tn, Declare face
Pre-processing
For each scale and rotation, the threshold, T(n),
decreases
When faces are detected, we remove the
corresponding portion of the masked input image
to try to avoid multiple and false detections
Algorithm is sensitive to errors made in the
pre-processing stage.
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Matched Filtering - Steps
Face Coordinates
Masked Input Image
Apply Matched Filter
Compare peaks To Threshold, T(n)
If Peak gt Tn, Declare face
Pre-processing
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Face Detection Steps and Progressive Masking

• After detecting peaks at the output
• of the matched filter, the following
• steps are taken
• Peaks within threshold range -gt faces.
• Face pixels are convolved with oval
• face mask of appropriate scale.
• Removes neighborhood of detected
• face pixel.
• After all processing, face pixels are
• consolidated into blobs by dilation.
• Finally, centroids of blobs deemed to
• be face centers.

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General Results
(Example result for Training_7.jpg)

• For training images, run time 80 110 sec.
• Detection results range from 83 - 100.
• Main Strengths Intuitive and (thus far)
  accurate.
• Main Weaknesses Sensitive to errors in
  pre-processing.

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Conclusions

• In most cases, effective use of color space
  face color
• relationships and morphological processing
  allowed
• effective pre-processing.
• For images trained on, able to detect faces with
  reasonable
• accuracy and miss and false alarm rates.
• Adaptive adjustment of template scale, angle,
  and threshold
• allowed most faces to be detected.
• Decision Feedback Masking reduced multiple and
  false
• detection rate

If additional time, would have liked to

• Pursue the Eigenimage approach further with MRC
  or SVM.
• Explore use of Wavelet spaces for face/gender
  detection.

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References

• Bernd Girod, EE368 Class Lecture Notes, Spring
  2002-2003
• R. Gonzalez and R. Woods, Digital Image
  Processing 2nd
• Edition, Prentice Hall, 2002
• C. Garcia et al., Face Detection in Color
  Images Using
• Wavelet Packet Analysis.
• M. Elad et al., Rejection Based Classifier for
  Face
• Detection, Pattern Recognition Letters, V.23,
  2002.

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