Face Detection

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Face Detection

• EE368 Final Project
• Group 14
• Ping Hsin Lee
• Vivek Srinivasan
• Arvind Sundararajan

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Overview

• Introduction
• Methods used to detect faces
• Color segmentation
• Morphological Processing
• Template Matching And Clustering
• Results
• Techniques considered but not used

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Color Segmentation

• Use color information in the YCbCr domain

• YCbCr Color space effectively decorrelates the
  intensity and color information
• Each channel information is represented in
  discrete levels.

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MAP Rule

• Implement MAP decoder to determine skin from
  non-skin pixels
• D(I(x,y)) 1 if P(I(x,y) S)P(S) gt T P(I(x,y)
  NS)P(NS)
• 0 other wise

• Minimizes misclassification error

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Result of Color Segmentation

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Morphological Processing

• Reject blobs of small sizes, perform closing,
  remove holes

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Non-face Object Removal

• Use information about shape and location of
  objects in conjunction to reject non-face objects
  while minimizing rejection of faces
• Objects characterized by ?max/?min as a measure
  of length. (Independent of size, translation, and
  rotation of objects)

Example of non-face object removed by CCA
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Non-face Object Removal
Before and after rejection
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Template Matching

• Performed in the luminance domain using the FFT
• First attempt use the average of all face
  regions
• Features did not seem to align properly, hence
  this template was rejected

Rejected Template
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Final Templates Used

• Resample each face region to the same size before
  averaging.
• Include mirror images of each face region to
  produce a symmetric template (a).
• In addition, a non-symmetric partial template (b)
  is used to capture information about smaller and
  partially obscured faces in the image
• One template tests for symmetry, while the other
  tests for non-uniform illumination, and captures
  smaller faces as well.

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Clustering of Correlation Peaks

• The autocorrelation results for each template
  were first thresholded and then combined.
• Used heuristic techniques based on shape of the
  skin regions to group peaks.
• Any 2 peaks meeting a maximum distance criterion
  and connected by a line passing through only skin
  regions were grouped together as a face.

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Results of Grouping Correlation Peaks

• Before and after peak grouping

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Results Applied to the Original Image

• Image corresponding to the grouped peaks

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Final Results

Image 1 2 3 4 5 6 7
Score 20 22 24 22 24 24 21
Total 21 24 25 24 24 24 22
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Techniques Considered but not Used

• Fishers linear discriminant (FLD)
• Poor performance in rejection of false positives
  because detected non-face and face regions are
  not linearly separable
• Eigenfaces
• Produced results similar to template matching but
  at an increased computational cost

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Techniques Considered but not Used

• Support Vector Machines (SVM)
• Generated 470 face regions and 500 non-face
  regions each of size 49x55 pixels as training
  database
• Employed a Gaussian radial basis function (RBF)
  as kernel

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Samples of database images

• Faces
• Non-faces

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Results of SVM

• Produced decision regions that are too tightly
  bound to the training face samples and were not
  able to classify the faces in the other training
  pictures
• Including the SVM in the program would only slow
  down our runtime and would not produce noticeable
  improvements

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Conclusion

• Color segmentation in the YCbCr domain and
  morphological processing produced good estimates
  of face regions
• Implemented multi-resolution template matching
  and peak clustering to further distinguish
  different face regions from each other and from
  non-face regions
• Could have done more to reject false positives
  (MRC/neural networks to reject hand regions)

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