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