Facial feature localization

Presented by: Harvest JangSpring 2002

OutlineIntroductionAlgorithmEvaluationFuture work

IntroductionFace feature extraction

Low-bit-rate video codingHuman computer interactionHuman face recognition

Automatically facial features Accuracy VS Performance

AlgorithmStep 1: Check image is human face or notStep 2: Find the face boundaryStep 3: Find the eye regionStep 4: Find the horizontal nose positionStep 5: Find the position of irisStep 6: Find the vertical mouth position

Human face checkingUse eigenface method

40 images as training set15 eigenvector for representationSubtract the image with the mean imageProjection the image to the eigenvectorCalculate the distance between the eigenvector and the projection imageSelecting the threshold to reject image

Example

Distance=5223 Distance=4992 Distance=7677

Distance=4544 Distance=3729

*can’t find face boundary

Distance=4303

*can’t find eye region

Face BoundaryAssume the picture is simple backgroundUse SOBEL filter for edge detectionUse horizontal projection of the binary image to find left and right face boundaries

Sobel filter

Eye RegionUse vertical projection to find possible eye regionVerify by property of symmetric of two eyes

Vertical projection of the binary image

Horizontal Nose PositionUse dynamic method to binaries the image

Find the selective thresholdCheck the fill factorRobust to skin color

Use horizontal projection of this binary image

Dynamic binarizationUse intensity histogram to two peak

Skin intensityFeature intensity

Calculate the threshold for binaries with fill factor

skin intensity

feature intensity

Image histogram of the image

Example

Original image

Figure 1

Figure 3

Figure 2

Determine the nose position

Use horizontal projection of the new binary image regionCharacteristics

Three peak two valleys

3 peaks

2 valleys

Horizontal projection of the binary image region

Black line:

Final nose position

Position of irisDivide the eye region into two partsCompute normalized cross-correlation of image and the eye template at each partFind the maximum value (max = 1)

Left and right eye template Correlation result

Left and right part of the eye region

Position of mouthUse the aspect ratio to find

Distance (d) between two eyesDistance between the mouth and eye ( about 1.0d – 1.3d)

Position of mouthUse vertical projectionFind the minimum value

Vertical projection of the binary image mouth region

binary image of mouth region

EvaluationORL face database

40 subjects10 different photos for each subjects

MachineSun Ultra 5/400

97s for 400 photos

Evaluation – ORL face database

Correct #

Error rate(%)

Eye region

393 1.75

Nose pos 376 6.00

Left iris 336 16.00

Right iris 312 22.00

Mouth pos

321 19.75

Future workImprove the accuracy of finding irisDetect human face from a large imageDetect face from video/web cam (face-tracking)

Thank you!