Face Detection
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Transcript of Face Detection
Face Detection
EE368 Final ProjectSpring 2003
- Group 6 -Anthony Guetta
Michael PareSriram Rajagopal
OverviewProblem IdentificationMethods Adopted Color Segmentation Morphological Processing Template Matching EigenFaces Gender Classification
Color SegmentationUse the color informationTwo 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
Result of color segmentation using Global thresholding
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
Overlap exists in RGB space also
Sample Blue vs Green plot for face (blue) and non-face (red) data.
Results from initial quantization Common problems identified
Better Code book developed Problem areas broken up
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 facesRepeated EROSION is done with specific structuring element
Previous state stored to identify new regions when split occurs
Superimposed mask image with eroded regions for estimate of
centroids
Template MatchingData set has 145 male and 19 female facesNeed to identify region around estimated centroids as face or non-faceMulti-resolution was attempted. But distortion from neighboring faces gives false valuesSmaller template has better result for all face shapesTemplate used is the mean face of 50x50 pixels
Mean Face used for template matching
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
Result from template matching and thresholding. Rejected - Red ‘x’. Detected
Faces – Green ‘x’
EigenFace based detectionDecompose faces into set of basis imagesDifferent methods of candidate face extraction from image
EigenFaces
(a) (b)
Candidate face extraction (a) Conservative (b) multi-resolution 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 datasetSlight changes give incorrect resultsHence, only template matching was used
Gender classificationEigenfaces and template matching for specific face features do not yield good resultsOther features for specific females were used – the headbandTemplate matching was performed for itConservative estimate was done to prevent falsely identifying males as a female
The headband template
TrainingImage
FinalScore
DetectScore
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
Table of results for training images
Approx. 95% accuracy with about 75 seconds runtime
Training 1
Training 2
Training 3
Training 4
Training 5
Training 6
Training 7
ConclusionRGB Vector Quantization gave excellent segmentationMorphological processing gave good estimate of centroidsTemplate matching with illumination correction gave near perfect resultsSpecific female was identified with headband
Future ConsiderationsEdge detection to better separate the connected facesPreprocess the image in HSV space before codebook comparison to improve runtimeImprove rejection of highly correlated non-face objects
Thank YouQuestions ?
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Gender RecognitionFace Detection
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Gender RecognitionFace Detection