1. EE368 Final Project Spring 2003
Face Detection
EE368 Final Project
Spring 2003
- Group 6 -
Anthony Guetta
Michael Pare
Sriram Rajagopal

2. Overview Problem Identification Methods Adopted Color Segmentation
Morphological Processing
Template Matching
EigenFaces
Gender Classification

3. Color Segmentation Use the color information Two approaches:
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

4. Result of color segmentation using Global thresholding

5. Sample Blue vs Green plot for face (blue) and non-face (red) data.
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)
Use RGB as a 3-D vector and quantize the RGB space for the face and non-face regions

6. Results from initial quantization
Common problems identified

7. Better Code book developed
Problem areas broken up

8. Initial step of open and close performed to fill holes in faces
Elongated objects removed by check on aspect ratio and small areas discarded

9. 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

10. Superimposed mask image with eroded regions for estimate of centroids
Previous state stored to identify new regions when split occurs
Superimposed mask image with eroded regions for estimate of centroids

11. Mean Face used for 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

12. Sample correlation result
Illumination problem identified
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

13. Result from template matching and thresholding. Rejected - Red ‘x’
Result from template matching and thresholding. Rejected - Red ‘x’. Detected Faces – Green ‘x’

14. EigenFace based detection
Decompose faces into set of basis images
Different methods of candidate face extraction from image
EigenFaces
(b)
(a)
Candidate face extraction (a) Conservative (b) multi-resolution with side distortion

15. Sample result of eigenface
Sample result of eigenface. Red ‘+’ is from morphological processing and green ‘O’ is from eigenfaces

16. 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

17. 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

18. Table of results for training images
Final
Score
Detect
Number Hits
Num Repeat
Num False Positives
Distance
Runtime
Bonus 1 22 21
71.91 2 23
82.96 3 25
9.8625
80.48 4 24
81.15 5
9.5960
69.59 6
80.25 7
71.52
Approx. 95% accuracy with about 75 seconds runtime

19. Training 1

20. Training 2

21. Training 3

22. Training 4

23. Training 5

24. Training 6

25. Training 7

26. 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

27. 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

28. Thank You
Questions ?