facial feature localization
DESCRIPTION
Facial feature localization. Presented by: Harvest Jang Spring 2002. Outline. Introduction Algorithm Evaluation Future work. Introduction. Face feature extraction Low-bit-rate video coding Human computer interaction Human face recognition Automatically facial features - PowerPoint PPT PresentationTRANSCRIPT
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Facial feature localization
Presented by: Harvest JangSpring 2002
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OutlineIntroductionAlgorithmEvaluationFuture work
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IntroductionFace feature extraction
Low-bit-rate video codingHuman computer interactionHuman face recognition
Automatically facial features Accuracy VS Performance
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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
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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
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Example
Distance=5223 Distance=4992 Distance=7677
Distance=4544 Distance=3729
*can’t find face boundary
Distance=4303
*can’t find eye region
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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
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Eye RegionUse vertical projection to find possible eye regionVerify by property of symmetric of two eyes
Vertical projection of the binary image
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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
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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
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Example
Original image
Figure 1
Figure 3
Figure 2
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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
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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
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Position of mouthUse the aspect ratio to find
Distance (d) between two eyesDistance between the mouth and eye ( about 1.0d – 1.3d)
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Position of mouthUse vertical projectionFind the minimum value
Vertical projection of the binary image mouth region
binary image of mouth region
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EvaluationORL face database
40 subjects10 different photos for each subjects
MachineSun Ultra 5/400
97s for 400 photos
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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
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Future workImprove the accuracy of finding irisDetect human face from a large imageDetect face from video/web cam (face-tracking)
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Thank you!