Face Detection Using Color Thresholding and Eigenimage Template Matching Diederik Marius Sumita Pennathur Klint Rose.

Face Detection Using Color Thresholding and

Eigenimage Template Matching

Diederik MariusSumita Pennathur

Klint Rose

Approach

Input Image

YCbCrThresholding

Binary ImageProcessing

Separationof “Blobs”

SizeThreshold

Homogeneity/Aspect Ratio

ThresholdCross-covariancewith Eigenimage

DuplicateDetectionRemoval

Rejection

Match Face Location to Input Image

Indi

vidu

al b

lobs

x10

Skin Segmentation Used distribution of Cr and Cb

values for faces vs. background to thresholdSkin region: 105<Cr<135

140<Cb<165

Binary Image Processing Erosion and dilation with face-shaped

segmentation element Removes small foreground and

background objects Delineates between larger regions

Separation and Rejection Larger regions labeled and

separated Series of Rejection Thresholding:

Size of regions (>3000 total pixels) Aspect Ratio (0.5 <AR< 1.8 ) St. Dev. of image values (60<<100)

Size based thresholding

Coupled AR and St. Dev. based thresholdin

g

Eigenimages Computed from set of “good” faces Employed Sirovich-Kirby method to calculate first 10 eigenimages Eigenimage #2: most accurate location of center of face Used eigenimage #2 exclusively

1 2 3 4 5 6 7 8 9 10

Template Matching Found cross-covariance peak and

marked as potential center Removed face sized area from

image Found new highest peak - repeated

10x

False Detection Removal Thresholded detected peaks

to obtain potential faces only Determined whether

multiple peaks belong to the same person (neck, etc)

Rejection criteria: Near the same y-axis and within a predetermined vertical distance of a previous point

Rejectedpoints

Typical Detection Result Detection problems: rotated faces,

small faces, hands, faces in lower 1/3

Results Average run time: 100 seconds 95% of faces found 4.4% false positives

# Faces # Correct False Pos False Neg % Correct % FalseTraining Image 1 21 21 1 0 100 4.8Training Image 2 23 21 5 2 91 21.7Training Image 3 23 22 0 1 96 0.0Training Image 4 24 22 0 2 92 0.0Training Image 5 24 23 0 1 96 0.0Training Image 6 24 23 0 1 96 0.0Training Image 7 22 19 1 3 86 4.5Total 161 151 7 10 95 4.4

17

16

5 (0)4 (19)15

5 (0)4 (19)14

5 (0)4 (19)13

5 (0)4 (19)12

5 (0)15 (08)11

2 (1)2 (20)10

2 (1)1 (22)9

1 (2)13 (14)8

5 (0)12 (16)7

5 (0)11 (17)6

5 (0)14 (13)5

5 (0)9 (18)4

5 (0)9 (18)3

5 (0)2 (20)2

2 (1)4 (19)1

Gender RecognitionFace Detection

17

16

5 (0)4 (19)15

5 (0)4 (19)14

5 (0)4 (19)13

5 (0)4 (19)12

5 (0)15 (08)11

2 (1)2 (20)10

2 (1)1 (22)9

1 (2)13 (14)8

5 (0)12 (16)7

5 (0)11 (17)6

5 (0)14 (13)5

5 (0)9 (18)4

5 (0)9 (18)3

5 (0)2 (20)2

2 (1)4 (19)1

Gender RecognitionFace Detection