Transcript Document 7748044

Face Detection
EE368 Final Project
Spring 2003
- Group 6 Anthony Guetta
Michael Pare
Sriram Rajagopal
Overview
Problem Identification
Methods Adopted
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Color Segmentation
Morphological Processing
Template Matching
EigenFaces
Gender Classification
Color Segmentation
Use the color information
Two approaches:
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Global threshold in HSV and YCbCr space using set
of linear equations. Lot of overlap exists
(a)
(b)
Clustering in (a) YCbCr and (b) V vs. H space. Red is non-face and
blue is face data
Result of color segmentation using Global
thresholding
Overlap exists in RGB space also
Sample Blue vs Green plot for face (blue)
and non-face (red) data.
Second approach involves RGB vector
quantization (Linde, Buzo, Gray)
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Use RGB as a 3-D vector and quantize the RGB
space for the face and non-face regions
Results from initial quantization
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Common problems identified
Better Code book developed
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Problem areas broken up
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Initial step of open and close performed to fill
holes in faces
Elongated objects removed by check on aspect
ratio and small areas discarded
Morphological Processing
Segmented and processed Image
consists of all skin regions (face, arms
and fists)
Need to identify centers of all objects,
including individual faces among
connected faces
Repeated EROSION is done with
specific structuring element
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Previous state stored to identify new
regions when split occurs
Superimposed mask image with eroded
regions for estimate of centroids
Template Matching
Data set has 145 male and 19 female faces
Need to identify region around estimated
centroids as face or non-face
Multi-resolution was attempted. But distortion
from neighboring faces gives false values
Smaller template has better result for all face
shapes
Template used is the mean face of 50x50
pixels
Mean Face used for
template matching
Illumination problem identified
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Top has low lighting, lower part is brighter
Left and right edges of images do not have people
2-D weighting function for correlation values
applied
2-D weighting function
Sample correlation result
Result from template matching and thresholding.
Rejected - Red ‘x’. Detected Faces – Green ‘x’
EigenFace based detection
Decompose faces into set of basis images
Different methods of candidate face
extraction from image
EigenFaces
(a)
(b)
Candidate face extraction (a) Conservative (b) multiresolution with side distortion
Sample result of eigenface. Red ‘+’ is from
morphological processing and green ‘O’ is from
eigenfaces
Minimum Distance between vector of
coefficients to that of the face dataset
was the metric.
It depends very much on spatial
similarity to trained dataset
Slight changes give incorrect results
Hence, only template matching was
used
Gender classification
Eigenfaces and template matching for specific face features do
not yield good results
Other features for specific females were used – the headband
Template matching was performed for it
Conservative estimate was done to prevent falsely identifying
males as a female
The headband template
Table of results for training images
Training
Image
Final
Score
Detect
Score
Number
Hits
Num
Repeat
Num
False
Positives
Distance
Runtime
Bonus
1
22
21
21
0
0
15.9311
71.91
1
2
22
21
23
0
2
13.6109
82.96
1
3
25
25
25
0
0
9.8625
80.48
0
4
22
22
24
0
2
11.3667
81.15
0
5
24
24
24
0
0
9.5960
69.59
0
6
23
23
23
0
0
11.5512
80.25
0
7
22
21
21
0
0
14.1537
71.52
1
Approx. 95% accuracy with about 75 seconds runtime
Training 1
Training 2
Training 3
Training 4
Training 5
Training 6
Training 7
Conclusion
RGB Vector Quantization gave excellent
segmentation
Morphological processing gave good
estimate of centroids
Template matching with illumination
correction gave near perfect results
Specific female was identified with
headband
Future Considerations
Edge detection to better separate the
connected faces
Preprocess the image in HSV space
before codebook comparison to improve
runtime
Improve rejection of highly correlated
non-face objects
Thank You
Questions ?
Face Detection
Gender Recognition
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